In-depth assessment of an eastern Indian and a westernSouth Pacific stocks of Antarctic minke whale from 2001
to 2014: A synthesis and summary (DRAFT)(EDITED BY THE INTERSESSIONAL CORRESPONDENCE GROUP)HIROTO MURASE1 (CONVENOR), HIDEHIRO KATO2, TOSHIHIDE KITAKADO2, KOJI MATSUOKA3, DEBRA PALKA4,
LUIS PASTENE3 AND ANDRE PUNT5
1National Research Institute of Far Seas Fisheries, Japan Fisheries Research and Education Agency, 2-12-4
Fukuura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan
2
Tokyo University of Marine Science and Technology, 4-5-7 Konan, Minato-ku, Tokyo 108-8477, Japan
3
The Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055, Japan
4
NOAA Fisheries, Northeast Fisheries Science Center, 166 Water Street, Woods Hole, Massachusetts, 02543, USA
5
School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, WA 98195-5020, USA

ABSTRACTIn-depth assessment of an eastern Indian and a western South Pacific stocks of Antarctic minke whale was carried out from 2001
to 2014 by the Scientific Committee of the International Whaling Commission (IWC/SC). The assessment covered a wide range of
topics including systematics, commercial and research catches, abundance estimates, spatial distribution patterns, stock structure,
biological information, population dynamics, food habit and energetic, pollutants and marine debris, and species interactions. This
document is s a synthesis and summary of the assessment over 13 years. The results of the Statistical Catch-at-age Analysis (SCAA)
revealed that (1) abundance increased from 1930 until the mid-1970, and declined over the period the mid-1970s until 1988 and (2)trends in abundance over the most recent 20 years are relatively flat. Although the primary focus at the start of this assessment wastrying to understand the abundance trends during observed in abundance surveys conducted during 1984 – 2004, but expanded toincreasing our knowledge on life history of this species which would contribute the management. The assessment also advanced
many aspects of analytical methods in the course of discussion in the IWC/SC.
KEYWORDS: ENVIRONMENT, FEEDING, POPULATION ASSESSMENT, POPULATION PARAMETERS, SOUTHERNHEMISPHERE, STOCK IDENTITY, REPRODUCTIONINTRODUCTION
The Antarctic minke whale (Balaenoptera bonaerensis Burmeister, 1867) resides in the Southern Hemisphere,
spending the southern summer feeding in waters all around the Antarctic and wintering between about 7° and 35°S
where breeding occurs. Observations of whales between 35° – 50°S suggested that the major proportion of animals
from the breeding grounds migrated south from October onwards to feeding areas in the Antarctic to arrive by
January. When feeding in the Antarctic the highest densities are along the ice edge with some animals within thepack ice.
In 1990 the last Comprehensive Review of Southern Hemisphere minke whales was completed. This
review focused on minke whales south of 60°S (IWC, 1990), which now is considered to be the Antarctic minke
whale. The effect of historical catches was evaluated in two ways: by comparing the total catches to the thencurrent abundance estimate and by HITTER, a computer program that evaluates effects of catches on a stock. At
that time, it was assumed there was a single stock in all six IWC Management Areas (Table 1) because results fordifferent approaches using samples from the commercial catch that were mostly from near the ice edge failed to
identify unambiguously isolated stocks within the Antarctic minke whales. Though the assessment evaluated theeffects of historical catches of minke whales within each the six Management Areas I–VI. Negatively biases
abundance was estimated for each Area using IDCR sighting survey data collected during 1979/80 – 1988/89. In
addition, Japanese scouting vessel data, mark-recapture, and catch per unit effort data were also considered as data
to estimate abundance. Demographic parameter values used in HITTER included age at recruitment=7, age atsexual maturity=7.5, and the natural mortality rate=0.105. Maximum sustainable yield (MSY) level was evaluatedat 60% and 80%. MSY rate was evaluated at 0, 1, 2, 3, and 4%. Those members who considered the HITTER
model appropriate, interpreted the results as, if the carrying capacity had been constant before 1972, for moststocks the exploited female component of the stock was at the high end of the range 50-100% of carrying capacity.
Those members who considered that the only useful indication of the status of the stocks came from a comparisonof the ‘current’ stock estimated with the total cumulative catch concluded that the abundance of minke whales inAreas V and VI (i.e., the major part of the Pacific sector) had been little changed by those catches. Also theabundance in Area I (the eastern sector of the Pacific) and Area II (in the South Atlantic) will not have been1

affected to an extent which would raise questions as to whether the historic rates of exploitation had been too high.
However, they believed that Areas III and IV (covering the South Atlantic and Indian Ocean sectors) haveexperienced catches that raise the question of whether lower rates of exploitation would have been desirable. They
added that there had been a tendency for catching to concentrate on the Area III/Area IV boundary. Such catches
could have led to greater depletion in the boundary region if it did not in fact divide two stocks which mix fully
and rapidly within the greater areas of the putative stock divisions.
In 2000 the Scientific Committee agreed to update this review and start an in-depth assessment that would
evaluate the status of the minke whales involving an examination of current stock size, recent population trends,
carrying capacity and productivity (IWC, 2001a). In particular, it was considered urgent to address trend-related
issues to provide up-to-date estimates of minke whale abundance. At that time, it was noted that while the estimatesof the minke population sizes accepted in the Comprehensive Assessment in 1990, which totalled 760,000 andwere obtained using IWC/IDCR data from 1982/83 to 1989/90, were the best available at the time of the years
surveyed, these estimates were no longer appropriate estimates of current minke whale abundance. This was
because some initial crude extrapolations of the incomplete third circumpolar set of surveys led to a point estimatethat was appreciably lower than the total of the previously agreed point estimates by Area from the Comprehensive
Assessment.
At that time, there was a long list of plausible hypotheses that may explain the apparent decline and it
was not possible to know the implications of this trend to the management of Antarctic minke whales and to theirecosystem (IWC, 2003). That list of plausible hypotheses consisted of:•Factors related to the population surveyed (e.g., changes in survey spatial-temporal coverage over years;changes in the location of the ice edge; or changes in the animal’s distribution within the survey area andoutside (within the ice and north of 60S).

•

Factors related to survey process (e.g., changes over the years in the proportion of schools classified as‘like-minke’, probability of observing animals on the track line [g(0)], bias of Closing versus independent
observer modes, or analysis options).

•

Factors that could cause a real decline in abundance (e.g., increase in natural mortality or decrease inrecruitment rate due to say increase in killer whales, pollution or disease; mortality due to commercial
and scientific whaling; incidental mortality from bycatch and ship strikes; overshooting carrying capacity;
decrease in carrying capacity due to lower krill populations or competition from other predators; orchanges in climatic conditions).

It took until 2014 to deal with all the issues, develop new methods, incorporate more recent data and put
them together into an updated assessment of the status of the minke whales and update the state of knowledge of
how the minke whales relate to their environment. This document summarizes the state of knowledge of the minke
whales as related to systematics, commercial and research catches, abundance estimates (from IDCR/SOWER,
JARPA/JARPAII, and joint icebreaker/aerial surveys), spatial distribution patterns, stock structure, biologicalinformation (including age, growth, morphology, maturity, and reproduction), population dynamics (using virtual
population analysis (VPA) and statistical catch-at-age (SCAA) techniques), food habit and energetics (including
consumption and body condition trends), pollutants and marine debris, and species interactions. This document
focus on an eastern Indian and a western South Pacific stocks of Antarctic minke whale which are distributed from
Area III East to Area VI West (Table 2). In-depth assessment of Antarctic minke whale in the rest of the Antarctic
was not completed in that period because of scarcity of data (IWC, 2015b). In the context of the IWC/SC, in-depth
assessment or comprehensive assessment can be considered as an in-depth evaluation of the status of all whale
stocks in the light of management objectives and procedures (IWC, 1987). It would include the examination ofcurrent stock size, recent population trends, carrying capacity and productivity.
SYSTEMATICS
Until recently, only one species of minke whale was thought to exist, Balaenoptera acutorostrata Lacèpéde, 1804,
however Rice (1998) reviewed both morphological (e.g. Williamson, 1959; van Utrecht and van der Spoel, 1962;
Kasuya and Ichihara, 1965; Omura, 1975; Best, 1985) and genetic (e.g. Wada et al., 1991; Arnason et al., 1993;
Pastene et al., 1994) data collected from extant minke whale populations and re-specified two species, the Antarctic
minke whale B. bonaerensis Burmeister, 1867, which is restricted to the Southern Hemisphere, and the common
minke whale B. acutorostrata Lacèpéde, 1804, which is distributed globally. Furthermore, he recognized threesub-species of the common minke whale, one in the North Pacific, one in the North Atlantic and one in the Southern
Hemisphere (see below). In the Southern Hemisphere, the common minke whale is referred to as the ‘dwarf’ or‘diminutive’ minke whale (Best, 1985; Arnold et al., 1987; Kato and Fujise, 2000; Kato et al, 2015). The two2

species in the Southern Hemisphere have not been confused during the sighting survey with closing mode becauseof the distinctive morphological features of the dwarf minke whales reported by those authors.
The Scientific Committee of the International Whaling Commission (IWC) accepted the recognition ofthese two species but deferred a decision on other nominal taxa until the completion of a worldwide review ofgenetic and non-genetics information of minke whales (IWC, 2001b).
Subsequent worldwide genetic analyses of minke whales based on mitochondrial DNA sequences
provided further evidences for the separation of the two species, B. bonaerensis and B. acutorostrata, and at least
three sub-species of the common minke whale as recognized by Rice (1998) (Fig.1, Pastene et al., 2010). It has
been hypothesized that the two-species diverged in the Southern Hemisphere less than 5 million years ago and thatthe current sub-species of B. acutorostrata diverged after the Pliocene some 1.5 million years ago (Pastene et al.,2007).
Genetic analyses based on microsatellites also provided evidence for the separation of species and subspecies of minke whale (Glover et al., 2013). As noted above, B. bonaerensis distributes in the Southern
Hemisphere however recent genetic studies provided evidence of migration of individuals of this species to the
Arctic Northeast Atlantic (Glover et al., 2010; 2013). Whether these migrations represent contemporary events, or
have occurred at a low frequency over many years, remain open.
The body length at physical maturity for North Pacific common minke whale was estimated in 7.5m formales and 8.0m for females (Kato, 1992); for North Atlantic common minke whale it was estimated in 8.2m formales and 8.8m for females (Jonsgard, 1951); for dwarf minke whale it was estimated in 6.6m for males and 7.0m
for females (Kato and Fujise, 2000; Kato et al., 2015); and for Antarctic minke whale, it was estimated in 8.5m
for males and 9.2m for females (Bando, personal communication). A report is available on variation in the color
pattern of white patch on the flippers between North Pacific and North Atlantic common minke whales (Nakamura
et al., 2014) and other authors have reported on differences in external appearance, body length and proportion,
osteological features and other biological aspects among minke whale species and sub-species (Kato and Fujise,
2000; Kato et al., 2015).
The Committee of Taxonomy of the Society for Marine Mammalogy listed the following species andsub-species of minke whale (Committee on Taxonomy, 2016):
Balaenoptera acutorostrata Lacèpéde, 1804. Common minke whale
B. a. acutorostrata Lacèpéde, 1804. North Atlantic minke whale
B. a. scammoni Deméré, 1986. North Pacific minke whale
B. a. un-named subsp. Dwarf minke whale
Balaenoptera bonaerensis Burmeister, 1867. Antarctic minke whale
For the Antarctic minke whale which is the main target of this review, Perrin and Brownell (2009)
provided a brief description of the morphological characteristics of the Antarctic minke whales. The rostrum isvery narrow and pointed and there is a single ridge on the head. The dorsal fin is relatively tall and falcate and islocated relatively far forward on the posterior one-third of the body (Fig. 2). Baleen plates are black on the left
beyond the first few plates and on the right, they are white in the first third and black in the rear two-thirds of therow.
While further genetic and morphological/morphometric studies are required to elucidate further thetaxonomic status within B. acutorostrata, including the dwarf minke whale, there is agreement among scientists
on the taxonomic status of B. bonaerensis, the species targeted for the in-depth assessment by the ScientificCommittee of the IWC.
CATCH BY COMMERCIAL AND SCIENTIFIC EXPEDITIONSCatch data for this in-depth assessment were extracted from the IWC individual catch database (IWC, 2005).
Number of Antarctic minke whales taken by commercial and scientific expeditions in the Indo-Pacific sector of
the Antarctic (35°E-145°W) from 1956/57 to 2014/15 and their geographic positions are shown in Figs. 3 and 4.
Data for these figures are extracted from the latest version of the database (Allison, 2016) at the time of the
publication of this paper. The first catch in this sector was recorded in 1956. UK took 3 individuals from 1956/57
to 1959/60 but vast majority of them were taken by USSR and Japan throughout the rest of period. Commercial
whaling targeting on this species has been suspended since 1986/87 after the IWC moratorium adoption in 1982.
Scientific sampling of this species has been conducted since 1987/88 (see below for more details). Maximum
number of catch in the period was recoded as 5,941inidividuals in 1976/77.

3

SURVEYS
IDCR/SOWER
The IWC Southern Hemisphere minke whale assessment cruises (IDCR: International Decade of Cetacean
Research and SOWER: Southern Ocean Whale and Ecosystem Research) have been conducted since 1978/79 in
the Antarctic regions of all six IWC management Areas for baleen whales (covering all, or more recently, part, of
one Area each season). In total, 32 shipborne surveys including the total of 4,340 ship-days, the total of 70 vessels,
and the total of 240 international researchers from 15 nations have been completed, which fall into threecircumpolar sets: 1978/79-1983/84 (the first circumpolar survey; CPI), 1985/86-1990/91 (the second circumpolar
survey; CPII) and 1991/92-2003/04 (the third circumpolar survey; CPIII), and experiment cruises from 2004/05
to 2009/10. The 1984/85 and 2004/05-2009/10 surveys were devoted mostly to experiments and were excluded
when estimating abundance for this in-depth assessment. Although the primary aim of the surveys has been to
estimate minke whale abundance, all cetacean sightings are recorded, which makes it possible to estimate
abundance for species other than minke whales. The survey methodologies up to 2000/01 were reviewed by
Matsuoka et al. (2003).
Over the years, there have been two major and some minor modifications to the survey design as a resultof the development of survey procedures. These developments represent the best possible compromise between
statistical needs and logistics. From 1985/86, the beginning of the second circumpolar set of cruises, theprogramme (initially a combination of Discovery marking and sightings) became essentially a dedicated linetransect systematic sightings cruise only. Passing mode with independent observer (IO mode) was introduced on
an experimental basis in 1985/86 and routinely covered half of the planned trackline from 1986/87. Prior to
1985/86, the surveys were conducted only in Closing mode in principle. During IO mode, a primary observer was
stationed in the independent observer platform (IOP) in addition to two primary observers in the barrel. IO mode
was introduced to provide data for the estimation of the probability that a school on the trackline is sighted (g(0)).
Modification of the survey design from the third circumpolar set of cruises (from 1991/92), to cover the whole
region south of 60°S in the Antarctic resulted in a change in emphasis of the latitudinal coverage, especially in
Areas I, II, III and V.
A series of experiments was carried out from 2004/05 to 2009/10 to address problems encountered withthe analysis of previous cruises. These included Bucland-Turnock survey method (Bucland and Turnock, 1992),
school size estimation experiments (terms as SS-II and SS-III) and visual dive time experiment. Cooperative
surveys with Japanese ice breaker and Australian aircraft were also conducted during this period (see below for
more details).
The sightings data from CPI to CPIII were encoded and validated and were contained in a databasepackage DESS 3.5 (IWC Database-Estimation Software System v 3.5, Strindberg and Burt, 2004). A standard data
set was extracted from DESS for abundance estimation (Burt, 2004).
The programme has also enabled collection of biopsy, photo-identification, oceanographic and acoustic
samples. It is concluded that the programme has developed and established standard sighting procedures and has
also improved the precision of whale identification standards in the Southern Ocean. This seems appropriate, given
the quantity of data available and information on their overall distribution in the Southern Ocean during the survey
period (Matsuoka et al., 2003).
JARPA/JARPAII
Government of Japan conducted the Japanese Whale Research Program under Special Permit in the Antarctic(JARPA) from 1987/88 to 2004/05 under the Article VIII of the International Convention for Regulation of
Whaling (ICRW). There were two original objectives: (1) estimation of biological parameters to improve the stock
management of the Southern Hemisphere minke whale (this included age-specific natural mortality coefficientand reproductive parameters such as age at sexual maturity and their changes) and (2) elucidation of the role ofwhales in the Antarctic marine ecosystem (Government of Japan 1987; 1989). To address objective 1, age-specificnatural mortality coefficient and reproductive parameters such as age at sexual maturity and their changes were
examined. The abundance of each whale species and the diet of the Antarctic minke whale were examined toaddress objective 2. The third objective, (3) elucidation of the effect of environmental change on cetaceans, wasadded in 1995/96 in response to the Commission’s resolution regarding to environment and pollution (Governmentof Japan 1995). The fourth objective, (4) elucidation of the stock structure of the Southern Hemisphere minke
whales to improve stock management was added in 1996/97 (Government of Japan 1996). Initially, the surveys
were conducted each year in alternating Areas IV and V. The survey area was expanded to include Area III East
and Area VI West from the 1995/96 to improve the study on stock structure of Antarctic minke whales which
related to the fourth objective. Subsequently, Areas III East + IV and Areas V + VI West were surveyed alternately

4each season. Two or three sighting and sampling vessels (SSVs) conducted sighting and sampling survey. A
dedicated sighting vessel (SV) was introduced from 1991/92 season. Sampled Antarctic minke whales were
examined on a research base vessel. General survey methodology was reviewed by Nishiwaki et al. (2006). Dataand results from the JARPA were reviewed in two specialist workshops held by the IWC: an Intersessional working
group meetings held in May 1997 (IWC JARPA mid-term review) (IWC, 1998) and the final intersessional
workshop held in December 2006 (IWC JARPA final review) (IWC, 2008).
Government of Japan conducted the Japanese Whale Research Program under Special Permit in the
Antarctic —Phase II (JARPAII) from 2005/06 to 2013/14 under the Article VIII of the ICRW. There were four
objectives of JARPAII: (1) monitoring of the Antarctic ecosystem, (2) modelling competition among whale species
and future management objectives, (3) elucidation of temporal and spatial changes in stock structure and (4)Improving the management procedure for Antarctic Minke whale stocks. Areas III East + IV and Areas V + VI
West were surveyed alternately each season. Two or three SSVs conducted sighting and sampling survey while
one or two SVs were conducted sighting survey. Sampled Antarctic minke whales were examined on a researchbase vessel. General survey methodology was reviewed by Nishiwaki et al. (2014). Data and results from the
JARPAII from 2005/06 to 2010/11 were reviewed in a specialist workshop held by the IWC (IWC, 2015a).
Icebreaker/Aerial surveys
Sighting data obtained by several icebreaker surveys were considered in the period of in-depth assessment. These
were platforms of opportunity (PoP) surveys rather than dedicated sighting surveys. A PoP survey was carried
from US icebreaker, the Nathaniel B. Palmer, between 150°W and 70°W in February-March in 1994 (Ainley etal., 2007). Cetacean sighting data were recorded in sea ice field between 77°50'E and 150°50'E in NovemberDecember in 1999 by Australian icebreaker, the Aurora Australis, as a part of the Antarctic Pack Ice Seal (APIS)
circum-Antarctic surveys and the Southern Ocean Cetacean Ecosystem Program (SOCEP) (collective termed as
APIS/SOCEP) (Thiele et al., 2002). Cetacean sighting data collected in the Ross Sea by the Nathaniel B. Palmerin 2004 as a part of the US Antarctic Slope (AnSlope) experiment were also presented to the IWC/SC (Thiele etal., 2005). Cetacean sighting surveys in sea ice field between 40°E and 150°E was conducted by Japanese
icebreaker, the Sirase, in 2004/05 (Shimada and Kato, 2005). The survey was conducted collaboratively with
IDCR/SOWER (Ensor et al., 2005). Three helicopter surveys with approximately 2 hours flight for each were also
conducted from the Sirase.
Dedicated cetacean sighing surveys using Australian fixed-wing aircraft were conducted between 93°E
and 113°E in 2008/09 (Kelly et al., 2009) and 2009/10 (Kelly et al., 2010) following a trial survey in 2007/08(Kelly et al., 2008). These surveys were conducted collaboratively with IDCR/SOWER (Ensor et al., 2008; 2009;
Sekiguchi et al., 2010).
ABUNDANCEIDCR/SOWER
IWC standard methodology
Abundance estimates of Antarctic minke whales from the 1978/79 to 1997/98 were presented to 53 IWC/SC basedon the IWC standard methodology (Branch and Butterworth, 2001). which was developed by the IWC/SC overyears (IWC, 2002). The results brought a number of methodological issues to be considered. These included school
size estimation (Brandão et al., 2001), data pooling by vessel and/or strata to estimate effective search half-width(esw) and mean school size (Burt and Hughes, 2002; Hakamada and Matsuoka, 2002; Matsuoka and Hakamada,2002), like minke sightings (Mori et al., 2003), closing-IO mode correction factor (Brandão and Butterworth,
2002), overlap area covered by more than one survey in CPIII (Branch and Ensor, 2004; Branch 2005). These
points were addressed in the analysis when abundance was estimated using three completed circumpolar sets ofsurveys from 1978/79 to 2003/2004 (Branch, 2006). Abundance estimated by the standard methodology was
negatively biased because it was assumed that all schools on the trackline were sighted (g(0)=1).
Potential covariates for estimation
Effect of observer efficiency was investigated (Butterworth et. al., 2001; Mori et al., 2003) but it was concluded
that the effect contributes less to differences in g(0) than school size does (IWC, 2003). the effects of sighting
conditions (school size, sighting cue, latitude and sea state) on Antarctic minke whale abundance estimation
parameters (effective search half-width, sighting forward distance and mean school size) were investigated(Murase et al., 2004) and such factors were considered further in abundance estimation models taking account ofg(0). Mori et al. (2002) demonstrated strong evidence that g(0) depended on school size. New methods to5

incorporate covariates information into estimation of school size and esw were also presented (Borchers and Burt,
2002; Rademeyer and Butterworth, 2001).
Models taking account of g(0)Three new abundance estimation methods which can take account of g(0) were initially presented by Bravington(2002), Cooke (2002) and Okamura et al. (2002) at 54 IWC/SC. Later, these models were called as IM (integrated
model), SPLINTR (spatial line transect) method and OK (Okamura-Kitakado) method, respectively. SPLINTR
was also termed as BBM (big beautiful model) and BHWP (Bravington, Hedley, Wood and Peel) method in thecourse of the development. OK method adopted a design-based approach using a hazard probability model while
SPLINTR adopted a two-stage spatial modelling using a point independence detection function model. IM used ahazard probability model with a spatial model based on Fourier series for density. A simulation data set wereprepared to investigate performance and robustness of these new methods (Palka and Smith, 2003). OK method
outperformed SPLINTR for simple simulation scenarios, whereas SPLINTR outperformed OK for simulation
scenarios with complex spatial effects. An intersessional group was established in 55 IWC/SC to improve these
new methods along with the simulation data (IWC, 2004). Five Intersessional workshops were also held (IWC,
2009; 2010; 2012; 2013a). After 10 years of discussions, agreed abundance estimates for CPII and CPIII were
finally produced in 64 IWC/SC based on OK method with some bias corrections about spatial effects fromSPLINTR outputs (Table 3; IWC, 2013b). Additional variance (or process error) had also been considered since
55 IWC/SC (IWC, 2004) and it was incorporated in the agreed estimates. Two sets of estimates are given, surveyonce and CNB (Common Northern Boundary) because the northern extent of the surveyed regions differs between
CPII and CPIII. The survey-once estimates cover all of the surveyed regions in each CP series. The CNB estimates
exclude part of the surveyed regions in each series to ensure a consistent northern limit. The CNB estimates are
the most appropriate estimates for a comparison of abundance estimates between CPII and CPIII. The CNB
estimates are the basis for the Additional Variance (AV) calculations. The CV internal in the table shows theuncertainty associated with the abundance estimate of whales in the surveyed region at the time of the survey. The
CV with AV shows the uncertainty associated with the average number of whales present in the surveyed region
across the whole of that CP series. The CV with AV is more useful for most subsequent analyses. Only one set of
CVs are presented in the table because they are approximately the same for survey-once as for CNB.Estimate for
CPI was not considered in these new methods because no IO mode data was available from CPI to take accountof g(0). Abundance estimates in Areas III East, IV, V and VI West which were used in the population dynamic
model (see below for details) are shown in Table 4.
JARPA/JARPAII
Abundance for each stratum were estimated by school size based on 1987/88 and 1988/89JARPA data (Kasamatsu
et al., 1990; 1991) in order to obtain unbiased biological information such as age composition and segregation forthe minke whales (Kato et al., 1990; 1991) using approach in Kishino et al. (1991), given that the minke whales
were sampled under multi-stage stratified random sampling.
At the intersessional working group to review data and results special permit research on minke whalesin the Antarctic in 1997 (IWC, 1998), sighting survey procedure during JARPA and abundance estimate in Areas
IV and V based on the sighting surveys from 1989/90-1995/96 were provided by Nishiwaki et al. (1997). The
review meeting recommended that more research was required to develop a reliable method for adjusting fornegative bias in abundance estimates induced by under-sampling of high density areas resulting from the JARPA
survey design. The working group agreed that more research was required to develop a reliable method for
adjusting for the higher-density-under-surveying future of the JARPA survey design. Once this had been achieved,
the resultant abundance estimates should be useful both as absolute and relative indices (IWC, 1998).
At the intersessional workshop to review data and results from special permit research on minke whalesin the Antarctic in 2006 (IWC, 2008), Burt and Paxton (2006) review spatial modelling applied to JARPA data toadjust the underestimate of abundance raised at the working group in 1997. The workshop noted potential
difficulties in correcting for skip effects by applying spatial models to estimate abundance, and agreed that standard
design-based estimates were best at this stage even if those need some sorts of correction as put forward using
spatial modelling approach. Hakamada et al. (2006) presents updated abundance estimates for Antarctic minke
whale based on JARPA sighting data using IWC ‘standard’ methodology and the inter-mode calibration methodof Haw (1991), including consideration of the recommendation above. All estimates assumed g(0)=1. They also
conducted sensitivity analysis to consider unsurveyed area due to for example ice-edge movement and saw-tooth
tracklines that seemed parallel to ice-edge in response to recommendations at SC58 meeting (IWC, 2007). The
estimates of abundance in Areas IV and V presented were 44,564 (CV=0.291) in 2003/04 and 72,087 (CV=0.146)(in 2004/05), respectively. The estimated annual rates of increase and their 95% CIs in Areas IV and V were -60.42% [-4.02%; 4.59%] (1989/90-2003/04) and -1.54% [-4.91; 2.18%] (1990/91-2004/05), respectively, whichsuggested no significant increase or decrease in abundance trend were detected.
Recommendations were made at the workshop (IWC, 2008; Hakamada et al., 2013). Some
recommendations were related to improve abundance and trend estimate and their precisions and others are related
to factors that may affected abundance trend and variance estimation. They are (1) re-estimation of detection
functions in cases where the number of schools detected is small; (2) investigation of sensitivities to pooling acrossvessels to estimate effective search half width and mean school size; (3) investigation of a possible ‘shoulder’ inthe detection function; (4) variance estimation for surveys by the Sampling and Sighting Vessels (SSVs) datataking correlation among tracklines into account; (5) undertaking of sensitivity analyses to examine the effect of
portions of the trackline following contours of the ice edge; (6) abundance estimation taking the whale density inthe gaps between the two main survey strata to be zero; (7) extrapolation of density to the unsurveyed area withina stratum; (8) consideration of changes over time in order in which strata were surveyed; (9) estimation of‘additional variance’; and (10) revision of estimates of the annual rates of increase in abundance and their CVs
following suggestions (1)–(9).
Analyses of the IDCRSOWER surveys results by Okamura and Kitakado (2012) and by Bravington andHedley (2012) have pointed to values of g(0) being less than 1 for minke whales and shown that possible changesin g(0) over time can be important in estimating trends in abundance. Because independent observer mode was notconducted during JARPA.
Hakamada et al. (2013) revised abundance and trend estimate in Areas IV and V, respectively, taking
these recommendations into account. Log-linear models are used to adjust for different strata being surveyed at
different times of year over the duration of JARPA, with model selection being based on AICc. Effects on changesin g(0) over time is investigated for the JARPA abundance estimates by the application of a regression model,
developed from the results of the OK method to estimate Antarctic minke whale abundance from the
IDCR/SOWER surveys, which provides estimates of g(0) from the statistics of the minke whale school size
distribution in a stratum. Abundance estimates for Area IV range from 16,562 (CV = 0.542) in 1997/98 to 44,945(CV = 0.338) in 1999/00, while those for Area V. range from 74,144 (CV = 0.329) in 2004/05 to 151,828 (CV =0.322) in 2002/03. Estimates of the annual rates of increase in abundance are 1.8% with a 95% CI of [–2.5%,
6.0%] for Area IV and 1.9% with a 95% CI of [–3.0%, 6.9%] for Area V. Abundance estimate and trend wererobust to the effects related to the recommendations (3), (5), (6), (7) and (8) above. With adjustment to allow g(0)< 1 derived from the regression model, abundance estimates increase by an average of 32,333 (106%) for Area IV
and 89,245 (86%) for Area V, while the estimates of annual rates of increase and their 95% CIs change slightly to
2.6% [–1.5%,6.9%] for Area IV and 1.6% [–3.4%,6.7%] for Area V.
At the JARPAII review workshop (IWC, 2015a), approach in Hakamada et al. (2013) was applied to
JARPA and JARPNII data in Areas III East, IV, V and VI West (170°-145°W) between 1989/90 and 2008/09(Hakamada et al., 2014). Abundance estimate in Areas III East, IV, V and VI West with and without taking into
account model error were shown in Table 5. For the estimates that took the model error into consideration theannual rates of increase in abundance were 1.1% with a 95% CI of [-2.3%, 4.5%] for Area III East +IV and 0.6%
with a 95% CI of [-2.2%, 3.3%] for Area V+VI West. Estimates are robust the effect related to therecommendations (3), (5), (6), (7) and (8) above.
Sea ice field
It has long been known that Antarctic minke whales are distributed in sea ice field (Ainley, et al., 2007; Ensor,
1989; Naito, 1982; Ribic, et al., 1991; Scheidat, et al., 2011; Taylor, 1957; Thiele and Gill, 1999). However, no
shipboard sighting survey data to the south of ice edge was available from IDCR/SOWER and JARPA/JARPAII
because the vessels could not navigate in there. In these surveys, ice edge was defined by a level of ice cover thatprevented the survey from being conducted at nominal survey speed of about 11.5 knots (Matsuoka et al., 2003).
There was a number of attempts to estimate abundance to the south of ice edge in Areas III, IV, V and VI at thetime of these surveys (Murase and Kitakado, 2013; Murase and Shimada, 2004; Murase et al. 2005; Shimada andBurt, 2007; Shimada and Murase, 2002; Shimada and Murase, 2003; Shimada et al., 2001; Shimada et al., 2002).
Main objective of these studies was not to produce absolute abundance but to investigate magnitude of potential
numbers taking simple relationship between abundance and sea ice concentrations derived from satellite.
Relationship between abundance in survey area and sea ice extent was also investigated (Matsuoka et al., 2006;
Matsuoka et al., 2008; Matsuoka et al. 2009; Murase and Shimada, 2004; Murase, 2010; Shimada and Murase,2006). Abundance in sea ice field in portions of Area III (40°E-50°E) and Area IV (70°E-82°E) was tentatively
estimated by using data obtained by Japanese icebreaker, Shirase in 2004/2005 (Shimada and Kato, 2006; Shimada
and Kato, 2007). Abundance in sea ice field in portions of Area IV (93°E-110°E) was tentatively estimated byusing data obtained by Australian aerial surveys in 2008/2009 and 2009/2010 (Kelly et al., 2014).

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The Joint Symposium on High Latitude Sea Ice Environments was held as a pre-meeting of 57 IWC/SC
to review information on sea ice environments in the Arctic and Antarctic, and to develop means of incorporating
sea ice and similar data into analyses and models used by the Scientific Committee in its work on abundance
estimation, determining variance, resolving issues of habitat use and the implications of seasonal, interannual anddecadal variability in sea ice on cetacean populations and habitat (IWC, 2007). Methods and data for estimationof abundance in sea ice field was evaluated by Kelly et al (2012) while technical aspect of see ice data derived
from satellite were reviewed by Murase et al. (2012).
These studies revealed that considerable number of Antarctic minke whales were distributed in sea ice
field of this sector. However, absolute number could not be obtained in the period of the in-depth assessment. It is
recommended that aerial survey should be sighting surveys in sea ice field bearing mind points raised in Kelly etal. (2012).
SPATIAL DISTRIBUTION
Prior to this in-depth assessment, spatial distribution of Antarcitc minke whales in the Indo-Pacific sector wasinvestigated qualitatively either using overlay maps (Ichii, 1990; Matsuoka et al., 2003; Murase et al.,2002) orsimple statistical correlation (e.g. Kasamatsu et al., 2000). Results of spatial distribution studies using generalized
additive models which applied to data obtained by line transect surveys were presented in the period of assessment
after such a method was proposed (e.g. Hedley, 1999). The circumpolar spatial distribution of Antarctic minke
whales during CPII and CPIII cruises was estimated using (GAMs) (Murase et al., submitted). Geographicalfeatures (bathymetry and distance to upper slope) and climatological data at the surface (water temperature, sea
surface height, salinity, and chlorophyll, silicate, nitrate and oxygen concentrations) as well as longitude andlatitude, were used as explanatory variables in the model while number of individuals aggregated in 30 × 30 km
grid cells was used as response variable. The results revealed that Antarctic minke whales were distributed
throughout the indo-Pacific sector of CPII and CPIII but their regions of high density appeared to be reduced fromCPII to CPIII (Fig. 5). High densities were observed in the souther part of the sector especially around the Ross
Sea (south of 69°S between 160°E and 160°W). The relationship between the circumpolar spatial distribution of
Antarctic minke whales and their environment was also investigated with GAMs using CPI, II and III data sets
(Beekmans et al., 2010), but with maps of estimated spatial distribution only included for the Weddell and Ross
Sea regions. Spatial distribution of Antarctic minke whales in the Ross was investigated using JARPA data taking
account of spatial distribution of krill (Murase et al., 2013).
STOCK STRUCTURE
Background
The Antarctic minke whale, like all the other Southern Hemisphere baleen whales species apart from the Bryde’s
whale (B. edeni), was managed by the IWC on the basis of six geographical ‘Areas’. The IWC established these
Areas from the 1974/75 austral summer season, based mainly upon information from Mackintosh (1942; 1966) on
distribution of catches of blue, fin and humpback whales (see review by Donovan, 1991). These Areas were usedby the IWC for the implementation of the New Management Procedure (NMP) on baleen whale species. However,biological evidences for the particular boundaries are weak, especially for those species such as the Antarctic
minke whale, whose data were not considered when the original management Areas were established. In this regard,
important questions were formulated originally by Hoelzel and Dover (1989): ‘Are the Antarctic minke whales
found in two geographically distinct management Areas from two different genetic stocks?’ or ‘Are individuals
from more than one genetic stock present in a particular management Area? If so, what level of interchange may
have occurred between different genetic stocks?’ Several approaches were used in the past to identify genetic
stocks of this species in the Antarctic feeding grounds and to determine to what extent genetic stocks and IWC
management Areas coincide.
Studies on stock structure of the Antarctic minke whale started at the end of the decade of the 1970’s andresults of genetic and non-genetic analyses were revised by the IWC Scientific Committee (IWC SC) during thecomprehensive assessment (CA) of the species in 1990. All the analyses presented at the CA were based on
samples and data from commercial pelagic whaling in the Antarctic. Genetic studies were based mainly on
allozyme although studies based on mitochondrial and nuclear DNA were also conducted, and most of the analyses
involved small sample sizes from only Areas IV and V. Non-genetic studies revised in the 1990’s CA involved
morphology, catch and sighting distribution pattern, analysis of Discovery marks and ecological markers. Resultsfrom the different approaches failed to identify unambiguously any isolated stock in the Antarctic (IWC, 1991).
Studies on stock structure under the JARPA started after the CA. It was considered that samples taken by
JARPA were more useful for studies on stock structure than the commercial samples given the wider geographical8covering of the surveys, and because whales were taken along track-lines in a random mode design in contrast to
the commercial whaling samples analyzed in the 1990’s CA, which were taken mainly from areas of high density
near the ice-edge (Pastene, 2006). Initially the JARPA genetic studies on stock structure were based on mtDNA
Restriction Fragment Length Polymorphism (RFLP), and a considerable genetic heterogeneity in Areas IV and V
was found (Pastene et al., 1993; 1996).
Results of stock structure analyses under the JARPA
The rationale for the analyses on stock structure under the JARPA was as follow. Analyses of Japanese sighting
data obtained during 1976-1987 in low latitudinal waters of the Indian and western South Pacific Oceans suggested
that there were two areas of high density of Antarctic minke whale, one in the eastern Indian Ocean (west ofAustralia) and the other in the western South Pacific (east of Australia) (Fig. 6), which were proposed as possible
breeding grounds of this species (Kasamatsu et al., 1995). The Australian continent can be considered a naturalbarrier to gene flow for some large baleen whale species during the winter reproductive period. For example,
humpback whales (Megaptera novaeangliae) from western and eastern Australia are genetically differentiated forboth mitochondrial and nuclear DNA (Schmitt et al., 2012). The genetic differences between western and eastern
Australian humpback whale stocks can also be detected in the Antarctic feeding grounds south of Australia,
probably reflecting site fidelity of stocks to particular feeding areas in the Antarctic during the summer season(Kanda et al., 2014).
In the case of the Antarctic minke whale, no genetic samples are available from low-latitude areas of theeastern Indian Ocean and western South Pacific that could be enable an investigation of the stock structure of thisspecies. However, if whales from those two low-latitude localities are differentiated genetically, and they movedirectly to the south during their spring/summer migration to particular feeding areas in the Antarctic, then it would
be possible to study the stock structure of Antarctic minke whale based on JARPA/JARPAII samples collected in
the feeding grounds, as the stocks would be distinguishable genetically even during the feeding season (Fig. 6).
Results of the genetic and non-genetic analyses on stock structure under the JARPA were presented to
the JARPA final review workshop (IWC, 2008). The main analytical procedure was hypothesis testing under thenull hypothesis of panmixia. Different approaches were used such as mtDNA RFLP (12 six-base restriction
enzymes), microsatellite DNA (six loci), morphometric (10 external measurements), and mean length of physicalmaturity. Details of the laboratory and analytical approaches as well as a synthesis of the results were presented
by Pastene (2006). Whales in the eastern and western sectors of the research area were more differentiated thanthey were to whales in the central sectors, and this result was common for most of the approaches used. Therefore
the author concluded that the single stock scenario cannot be applied to Antarctic minke whales in the feeding
grounds of Areas III East-VI West. He concluded that the results were consistent with the occurrence of at least
two genetic stocks in the research area, which are probably related with the proposed breeding areas in the eastern
Indian Ocean and western South Pacific (Fig. 6). The following names were proposed for these stocks: Eastern
Indian Ocean Stock (I-Stock) and Western South Pacific Ocean Stock (P-Stock) (Pastene, 2006).
The JARPA final review workshop agreed that there were at least two stocks of Antarctic minke whales
present in the research area and that the data do not support the current IWC management Areas. The workshop
also agreed that the data suggest an area of transition in the region around 150-165°E across which there is an as
yet undetermined level and range of mixing (IWC, 2008). During the workshop several recommendations weremade to refine the analyses conducted on stock structure. One of them was that the transitional area be studied byfitting model where the fraction of whales belonging to one putative population is a function of the longitude atwhich it was sampled. This could be a simple logistic regression model coupled with two-product multinomial
models describing the allele frequencies in the two putative stocks either side of the transition area. It was notedthat this approach could also be extended beyond two populations, and incorporate both genetic and morphometricdata (IWC, 2008). This approach was taken into consideration when the analyses on stock structure were refined
under the JARPAII.
Refinement of the analyses on stock structure under the JARPAIIJARPAII was conducted during the austral summer seasons 2005/06/-2013/14 in the same research area coveredby JARPA. The analyses on stock structure were refined in two ways, the first implied additional laboratory work
for additional genetic markers, and the second the application of a new analytical approach following a
recommendation from the JARPA final review workshop.
All the Antarctic minke whales taken by JARPAII between 2005/06 and 2010/11 were sequenced for a
340bp-segment of the control region of the mtDNA (instead of the mtDNA RFLP used in JARPA) and genotyped
using twelve microsatellite loci (instead of the six used in JARPA). Results of the heterogeneity test for bothmarkers showed significant genetic differences between whales in two sectors, western (35-130°E) and eastern9(165°E-145°W), confirming that different stocks inhabit the Indian and Pacific sectors of the Antarctic (I and P
stocks). Microsatellite DNA analyses showed more dispersal in males than females, and also some degree ofannual variation (Pastene and Goto, 2016).
In response to a recommendation from the JARPA final review workshop Schweder et al., (2011)
developed an integrated approach for estimating longitudinal segregation of two stocks using different sources of
data: morphometric, microsatellite and mtDNA data. Under this approach the soft boundary (or transition area)suggested previously was allowed to vary by year and sex. A joint likelihood function was defined for theestimation of mixing proportions and statistical tests without assuming any baseline populations. The approach
was originally applied to the JARPA data (Schweder et al.., 2011) and subsequently to JARPA and JARPAII data
(Kitakado et al., 2014).
The results of this new analytical approach confirmed the occurrence of at least two stocks (I and P stocks)
in the JARPA/JARPAII research area. Furthermore the results indicated that the spatial distribution of the twostocks has a soft boundary in Area IVE (100-130°E) and VW (130-165°E), which change by year. Results alsosuggested possible sex differences in the pattern of distribution of the two stocks (Kitakado et al., 2014).
Therefore the structure of Antarctic minke whale in Areas III East-VI West appears to be more complex
than originally thought: at least two stocks with a wider mixing area, which change by year and sex (Fig. 7).

BIOLOGICAL INFORMATIONAge
Age of Antarctic minke whales is traditionally estimated by counting growth layer groups (GLGs) accumulated in
the earplug, assuming an annual deposition of growth layers (i.e. one pair of dark and pale laminae accumulated
per year) (Lockyer, 1984). Earplug age reading has been conducted from the beginning of exploitation of thisspecies in 1970s. Age reading error, i.e. the extent of bias and inter-reader variability among readers have been
discussed in the SC and a statistical model for quantifying age reading error was developed by Kitakado et al.
(2013) and results were incorporated in SCAA (Punt et al., 2013).
Another ageing method is the estimation by ratio of aspartic acid racemization in eye lens. This method
was developed for other baleen whale species such as fin (Nerini, 1983) and bowhead (George, et al., 1999) whales
and applied to Antarctic minke whales (Yasunaga, et al., 2014).
GrowthGrowth curve of I-stock and P-stock Antarctic minke whales were estimated by Bando et al. (2006) from JARPA
samples (Table. 6). Mean body length increased rapidly until around 7 years old and the growth ceased at around15-20 years old. Estimated asymptotic length was about 20cm larger in I-stock than P-stock, 8.63m and 8.45m for
I-stock and P-stock males and 9.17m and 8.93m for both stocks of females.
Physical maturity was determined based on the examination of the vertebral epiphysis of 6th dorsal
vertebra stained by 0.25% toluidine blue-O solution. Cartilage between epiphyses and centrum was observed by
naked eye or stereoscopic microscope and whales of which the epiphyses fused to the centrum were defined asphysically mature. The estimated age at 50% physical maturity of male was younger than female for both stocks(Table. 6). As in the case of asymptotic length, the body length at 50% physical maturity of I-stock was larger thanP-stock (Table. 6).
Morphology
Some morphological and morphometric studies were conducted on the Antarctic minke whale in the past using
data obtained during commercial pelagic operations (Doroshenko, 1979; Wada and Numachi, 1979; Bushuev,
1990). However, results of these analyses provided no evidence of unambiguous genetic differences between Areasin the Antarctic.
Morphological and morphometric analyses are useful tools to examine questions on stock structure as
demonstrated in the case of the North Atlantic common minke whale (Christensen et al., 1990). Fujise (1995)
conducted a preliminary morphometric analysis based on 16 external measurement of 1989/90 JARPA samples(n=326) in Area IV using Analysis of Covariance (ANCOVA), Principal Component Analysis (PCA) andCanonical Discriminant Analysis (CDA).

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For investigation of stock structure using non-genetic marker, Hakamada (2006) conducted morphometricanalysis using JARPA surveys in Areas III East, IV, V and VI West between 1987/88 and 2004/05. In the analyses,
ANCOVA and cluster analysis were applied to 10 external measurements from 2,629 male and 1,803 female
mature Antarctic minke whales. Results were broadly consistent with those in Fujise (1996) and those using genetic
marker (Pastene et al., 2006).
Schweder et al. (2011) developed an integrated approach for estimating longitudinal segregation of two
stocks using different sources of data: morphometric, microsatellite and mtDNA data. The approach dealt with
soft boundary between two stocks. The approach was originally applied to the JARPA data (Schweder et al., 2011)
and subsequently to JARPA and JARPAII data (Kitakado et al., 2014).
Kato and Fujise (2000) conducted analysis on morphometric variation among dwarf minke, Antarctic
minke and western North Pacific common minke whales using ANCOVA and CDA. Analysis revealed significant
difference in external body proportion and skull measurements among three forms of the minke whales.
Maturity
Sexual maturity of male is determined by examination of testis histological samples. Sexual maturity for females
is determined by the presence of corpora luteum or albicans in both ovaries. Three method has been developed toestimate age and body length at sexual maturity of Antarctic minke whales (Kato, 1987).
tm50%, Lm50%: Age/length at 50% of animals attained sexual maturity
tmov, Lmov: Mean age/length of females with first ovulation
tmp: Mean age estimated from transition phase in earplug
Tm50%/Lm50% and tmov/Lmov were estimated from JARPA samples based on two stocks (Table. 6,
Bando et al., 2006). The estimated body length at sexual maturity of I-stock animals was 10-20cm larger than Pstock for both sexes.
The long trajectory of tmp based on cohort was examined by several authors by using commercial whaling
and JARPA/JARPAII samples (Kato and Sakuramoto, 1991; Thomson et al., 1999; Zenitani and Kato, 2006;
Bando et al., 2014). The tmp declined from 14years in 1940s to 7years in 1960s and remained constant at 7-8years
till 1990s cohorts.
Reproduction
Bando et al. (2006) reported several biological parameters related to reproduction estimated by JARPA samples(Table. 6). Proportion of pregnant in matured female (PPF) was estimated as 92.9% and 85.4% for I-stock and Pstock, respectively. The PPF during JARPAII period remained high and no significant trend was detected for bothstocks (Bando and Hakamada, 2014). Annual ovulation rate, which is a maximum value of true pregnancy rate,
was 0.98 and 1.01 for both stocks, which might indicate high true pregnancy rate for Antarctic minke whales(Bando et al., 2006). Foetal male ratio was 51.8% and 46.8%, which did not differ from parity. Multiples was rare
for this species, only 8 among 1,142 and 9 among 717 cases of twin were observed for I-stock and P-stock,
respectively (Bando et al., 2006).
POPULATION DYNAMICS
BackgroundThe data for Antarctic minke whales have been analyzed using population dynamics models for over 40 years,
although the focus for population dynamics research for Antarctic minke whales has changed over time. The
earliest analyses aimed to provide a basis for setting catch limits and to explore whether the extensive exploitationof other baleen whale species led to the creation of a “krill surplus” and hence whether Antarctic minke whales
were increasing prior to the start of substantial exploitation in the early 1970s. The subsequent set of analysis
aimed to assess whether population dynamics models, along with data collected by the “Japanese Whale Research
Programme under Special Permit in the Antarctic” (or JARPA), could allow estimation of age-specific natural
mortality (M), as inferences regarding changes over time in minke whale abundance are sensitive to assumptions
regarding the value for M (Fig 1. of Punt, 2014). The most recent analyses have focused on directly estimatingchanges over time in carrying capacity and recruitment, and over age in natural mortality, and hence exploring thepossibility that the decline in recruitment is related to competition and other population dynamic-related factors(IWC, 2005).

11

The initial population models based on cohort analysis (e.g. Sakuramoto and Tanaka 1985, 1986) wereapplied to data for Antarctic Areas III and IV, treating the data for each Area separately. Subsequent analyses
based on ADAPT-VPA (Gavaris, 1988) considered the minke whales in Antarctic Areas IV and V as separate
populations (Butterworth and Punt, 1990; Bergh, 1991a,b; Butterworth et al., 1996, 1999, 2002; Mori et al., 2007).
However, the assumption that each Antarctic Area contains a separate stock was shown to be untenable based on
genetic (mtDNA and microsatellite) and non-genetic (morphometric, biological parameters) analyses (IWC, 2008;
Pastene, 2006). This led to the development of population dynamics models based on Statistical Catch-at-age
Analysis (SCAA) that can simultaneously model multiple stocks (e.g. Punt and Polacheck, 2005, 2006; Punt et al.
2014) and extension of the assessed area from Antarctic Areas IV and V to Areas III East, IV, V and VI West.
However, even the most recent assessments fail to allow for mixing of stocks in the assessed area, despite evidence
that such variation occurs (Kitakado et al., 2014)
The VPA and SCAA were modified over time in response to review by the Scientific Committee. Thus,
the following sections focus on the “final” versions of the two methods (Butterworth et al. (2002) and Punt et al.
(2014) respectively).
Virtual Population Analysis
In common with all VPA-type methods, the VPA developed for Antarctic minke whales assumed that the catchesat-age were measured without error and projected cohort numbers backwards from either the most recent year or
the oldest age. Initially, the VPA analyses were based on grouping and analyzing the catch-at-age data on a 3-age3-year basis to overcome small sample size problems for the statistical distribution underlying the estimator usedat that stage, although subsequently analyses were also conducted on a 1-age-1year basis. The analyses by
Butterworth et al. (2002) followed the methodology applied by Butterworth et al. (1999) and estimated agespecific natural mortality, a fishing mortality scalar for the last year with data (2001 for Antarctic Area IV and1998 for Antarctic Area IV), and selectivity for the JAPRA surveys and the commercial catches. The VPA wasfitted to the JARPA indices of relative abundance, the IDCR estimates (taken to be estimates of absoluteabundance), and the commercial and scientific catches-at-age.
The estimates of recruitment (and hence 1+ abundance) were estimated using the VPA to have increased
over time (from at least the 1940s) and peaked at the end of the 1960s and then declined and stabilized (Fig. 8).
This result was robust to whether the model is based on a 1-age-1-year or 3-age-3-year basis, as well as to the
choice of the plus-group for the catch-at-age data component of the likelihood function. The estimates of (ageindependent) natural mortality were 0.07yr-1 (90% CI 0.036-0.093) (Area IV) and 0.049 yr-1 (90% CI 0.0110.095) (Area V).
Statistical Catch-at-Age Analysis
Statistical catch-at-age analysis (Fournier and Archibald, 1982) involves developing a population dynamics modeland fitting it to the available data by maximizing an objective function. Punt and Polacheck (2005) noted that there
were several potential concerns with the ADAPT-VPA approach as implemented at that time that could be
addressed within the context of statistical catch-at-age analysis: (a) the catch age-composition data were assumed
to be known exactly when constructing the numbers-at-age matrix, (b) it was not possible to estimate the numbersat-age for some of the oldest ages in the earliest years for which commercial age-composition data were available
directly using back-projection, and (c) one of the key objectives of the past assessments of minke whales in Areas
IV and V was to estimate the Maximum Sustainable Yield rate (MSYR), but the VPA could not do this directly.
The most recent version of the SCAA (Punt et al., 2014) is sex-specific, can model multiple stocks
simultaneously, and allows for multiple fleets and regions, permits carrying capacity, the deviations in calf
numbers about the expected number of births, the growth curve, vulnerability, and the proportion of each stock ineach region to change over time. It includes multiple options for modelling time-dependence in natural mortality
and sex- and length-specific vulnerability. The model allows some of its parameters (such as the age-specificity
of natural mortality and the resilience of the stock-recruitment relationship) to be shared among stocks. The model
can be fitted to estimates of absolute and relative abundance, catch length-composition data, and conditional ageat-length data. The model is nominally fitted to the total catches by sex, but given the weight assigned to theassociated likelihood component, the fit is almost exact. The model can also be fitted to catch-at-age data, but thiswas not needed for the implementation for Antarctic minke whales. Punt (in review) concluded that this is themost complex stock assessment for any cetacean population. The objective function minimized includes thelikelihoods for each data type and penalties on time-variation in births, vulnerability, growth, carrying capacity,
and area-proportion deviations.
The application to Antarctic minke whales assumes that there are two stocks in Areas III East to IV West,
with the I (Indian) stock – assumed to be found in Areas III West, IV, and V West, and the P (Pacific) stock –12assumed to be found in Areas V East and VI West. The model includes 15 ‘fleets’ consisting of three whaling
types (Japan before 1987/88, Japan from 1987/88, and ex-Soviet Union) in each of the five areas considered in themodel. Two Japanese whaling types are considered so that the data for commercial and Scientific Permit catches
can be treated separately. The SCAA ignored the length-frequency data for the ex-Soviet Union fleet because ofconcerns regarding the reliability of these data (there are no age-composition data for this fleet) and vulnerability
for the ex-Soviet Union and the Japanese fleet were assumed to be the same. This latter assumption was madegiven information on possible misreporting of catch length distributions by the ex-Soviet Union (IWC, 2011).
The SCAA was able to mimic the available data well, although this was due in part to the large numberof parameters and the allowance for both overdispersion and age reading error. The results from the SCAA analysis(now for Stocks I and P, and with data until the 2012/13 austral summer) were qualitatively similar to those fromthe VPA (Figs 8 and 9). Specifically, the SCAA inferred that Antarctic minke whales in the assessed area increased
from 1930 until the mid-1970, and declined over the period the mid-1970s until 1988. The increase rate for totalabundance size was estimated to be 1.9% (SE 0.7%) annually for stock I and 2.1% (SE 1.1%) for stock P. The
extent of increase from 1945 to 1968 was estimated to be higher for stock P than for stock I. Although stocks I and
P do not match Antarctic Areas IV and V exactly, the results from the SCAA are more precise and the trend forstock P exhibits a continuing decline compared to the VPA. The increase in abundance was estimated to be due
primarily to an increase in recruitment owing to an increase in carrying capacity. However, carrying capacity wasestimated to have declined following a peak for both stocks, but the effect of this on recruitment and hence total
population size was much smaller for stock P than for stock I. The estimates of the recruitment (right panels of
Fig. 9) suggest that there have been periods of good and poor recruitment.
Natural mortality is a piecewise linear function of age (Fig. 10). The estimates of natural mortality
indicate that natural mortality is highest for the youngest and (particularly) oldest animals, with the estimate ofnatural mortality for age 15 0.048yr-1 (SE 0.005) for Stock I and 0.046yr-1 (SE 0.005) for stock P, which are lower
than the estimates from the VPA (Fig. C).
The SCAA can be used to estimate MSYR1+. The reference case SCAA estimates of MSYR1+ weregenerally very high (>0.2), but some of the sensitivity tests led to estimates of MSYR1+ which are essentially zero.
A primary reason for the inability to estimate MSYR1+ is that the stocks were estimated to be close to carrying
capacity throughout the assessment period for most of the SCAA analyses (Fig. 3 of Punt et al., 2014).
The results are generally insensitive to changes to specifications of the SCAA reference case analysis.
However, not allowing for time-varying growth led to a markedly faster estimated rate of increase for stock P andalso to higher estimates of natural mortality for very young animals for both stocks, while the rate of increase waslower over the initial years of the projection period when carrying capacity was assumed to be constant.
An original aim for developing the SCAA was to determine the reason or reasons for the decline inabundance of Antarctic minke whales. The SCAA does not provide definitive conclusions in this regard. However,
the results point to the possibility that carrying capacity has changed over time (first increasing then decreasing).
However, “carrying capacity” in the model relates to trends in at least four processes: pregnancy rates, infantsurvival rates, age-0 survival rates and changes in maturity – the data included in the SCAA do not allow theseprocesses to be distinguished.
Next steps
Although the SCAA-based assessment is “mature” in that it was under development for almost a decade and had
been refined through the suggestions and advice of the Scientific Committee, there remain areas for future work
as highlighted by Punt et al. (2014): (a) consideration of other stock structure hypothesis, including a soft boundary
in Areas IV-E and V-W, which depends on year and sex (Kitakado et al., 2014), (b) evaluating the performanceof the estimation method using simulations, (c) application of the model to broader range of areas within the
Antarctic, and (d) analyzing the estimates of deviations in calf survival rate to identify the likelihood of possibly
causal mechanisms for the changes in recruitment over time.
FOOD HABIT AND ENERGETICS
Food habits and consumption
The Antarctic minke whale, which grows up to 10m (Horwood, 1990), is the most abundant balaenopterid species
in the Southern Ocean with abundance estimated at 515,000 animals south of 60°S in austral summer (95%
CI:360,000–730,000). These estimates were based on sighting data collected between the 1992/93 and 2003/04
seasons (IWC, 2013). Like other balaenopterid species (except the Bryde’s whale), the Antarctic minke whale
spends its breeding season at lower latitudes in austral winter and migrates to the Southern Ocean to feed in austral

13

summer (Horwood, 1990; Kasamatsu et al., 1995). The Antarctic minke whale feeds mainly on Antarctic krill(Euphausia superba) in offshore waters (Kawamura, 1980; Bushuev, 1986; Ichii and Kato, 1991), and on ice krill(E. crystallorophias) on the coastal shelf along such areas as the Ross Sea and Prydz Bay (Bushuev, 1986; Tamura
and Konishi, 2009). Previous studies estimated the daily prey consumption by Antarctic minke whales in theSouthern Ocean on the basis of energy-requirement calculations (Lockyer, 1981; Armstrong and Siegfried, 1991;
Reilly et al., 2004).
Based on the Japanese Whale Research Program (JARPA) research from 1987/88 to 2004/05 seasons,the maximum weight of stomach contents were 125.7kg (3.1% of body weight) and 156.0kg (3.4% of body weight)
for immature male and female, respectively and 343.8kg (4.2% of body weight) and 321.2kg (3.6% of bodyweight) for mature male and female, respectively. The mean weight of fresh or lightly digested stomach contents
were 30.9±23.5kg (1.0% of body weight) and 43.0±31.5kg (1.0% of body weight) for immature male and female,respectively and 74.2±50.1kg (1.1% of body weight) and 76.3±54.6kg (1.0% of body weight) for mature male andfemale, respectively.
The daily prey consumption by the whales in each reproductive status group was estimated using energyrequirement and energy deposition. The daily prey consumptions during feeding season were 31.5×104 and68.3×104 kJ for immature and mature male, and 52.1×104 and 122.3×104 kJ for immature and mature female,respectively. When the mean energy value of prey of 4 473kJ kg-1 and the assimilation efficiency of 84 % wereconsidered, the daily prey consumptions during feeding season were 83.7 and 181.7kg for immature and mature
male, and 138.7 and 325.5kg for immature and mature female, respectively. These values were equivalent to 2.9
and 2.7 % of body weight for immature and mature male, and 3.7 and 4.0 % of body weight for immature andmature female, respectively. The total prey consumptions per capita during feeding season were 7.5 and 16.4 tons
for immature and mature male, and 12.5 and 39.1 tons for immature and mature female, respectively (Tamura andKonishi, 2009).
As next step, the uncertainty in several components involved in estimating the amounts and types of prey
consumed by whales was assisted by a recent review by Leaper and Lavigne (2007) and Tamura et al. (2009). The
several uncertainties (e.g. allometric relationships, body weight of whales, energy values of prey species,
assimilation efficiency and length of feeding period) in the estimations of prey consumption associated to therelevant parameters will be treated by Monte Carlo simulations. Recently, development of individual-based models
of cetacean foraging are progressing using the multi-sensor movement tag and telemetry data (Friedlaender et al.,2008, 2011, 2016). It is useful for understanding of the species-specific energetic costs of feeding by Antarctic
minke whales.
Body condition and the annual trend in energy storage
Whales generally accumulate energy as lipid in the blubber during the summer feeding period at high latitudes andmigrate to low latitude areas (Næss et al., 1998) spending the energy for reproduction and migration. The proxyof fat reserves in blubber have been used as an indication of body condition in whale studies (e.g. Lockyer . 1985,
1986; Lockyer and Waters 1986; Vikingsson, 1995; Koopman, 1998; Ichii et al., 1998; Haug et al., 2002). Blubber
thicknesses, blubber weight and girth have been used as the indices of previous studies and found to increase
through the feeding season (Lockyer, 1987; Vikingsson, 1995; Næss et al., 1998; Konishi et al., 2008). The
measurements of blubber thickness positively correlates with lipid content in the whole body fat and proven to be
dependable proxy for energy storage in whales by Lockyer et al. (1985). In the 18 years JARPA period, annual
trend in energy storage in the Antarctic minke whale was examined using minke catch data. This regression
analyses clearly showed that blubber thickness, girth and fat weight had been decreasing for this period. The
decrease per year is estimated at approximately 0.02 cm for mid-lateral blubber thickness and 17 kg for fat weight,
corresponding to 9% for both measurements over the 18-year period (Konishi et al., 2008). The IWC/SC agreed
that a decline in blubber thickness and in fat weight that was statistically significant at the 5% level had occurredin JARPA period (IWC, 2015b; Konishi and Walloe, 2015). As next step, several body condition analyses for the
Antarctic minke whales will be developed based on scientific permit researches by Japan.
POLLUTANTS AND MARINE DEBRIS
Honda et al. (1987) had proposed the hypothesis that 'yearly changes of age accumulation patterns of hepatic
mercury (Hg) in Antarctic minke whales suggest possible environment changes such as changes of foodavailability’ based on no observation of usual age-related accumulation of hepatic mercury in minke whales
taken during a period of early 1980’s, and Fujise et al. (1997), Watanabe et al. (1998) and Honda et al. (2006)
reported that the phenomenon continued until 1990’s due to a greater availability of food. Yasunaga et al.
(2006a) reported using sample taken until 2005 that Hg intake in young minke whales (1-15 years) significantly
decreased with sampling years, while that of individuals over 16 years old was stable, and Yasunaga et al.

14

(2014a) reported using sample taken until 2013 that hepatic Hg levels of minke whales of all age groups in Area
IV decreased significantly with research years and that of 15-26 year old whales in Area V increased
significantly. It is suggested that food availability of Antarctic minke whales in 2000’s may be change to those in
1980’ and 1990’.
Levels of polychlorinated biphenyls (PCBs), dichlorodiphenyl trichloroethanes and metabolites(DDTs), hexachlorocyclohexane isomers (HCHs), hexachlorobenzene (HCB) and chlordane compounds (CHLs)
in blubber of minke whales taken from Areas V and VI in a period from 1988/89 to 2004/2005 extremely slowly
decreased or maintained a steady state, while those of HCHs were drastically decrease in the period (Yasunaga etal., 2006b; Yasunaga et al., 2015). In comparison of those from Area V between from 1988/1989 to 2004/2005
and in 2010/2011, Levels of DDTs, HCHs, HCB and CHLs in Area V decreased significantly with year, while
the yearly trend of PCBs did not change significantly. HCHs levels in minke whales in 2010/2011 were similar
to those in the JARPA period from 1996/1997 to 2004/2005 however they were lower than those in the JARPA
period from 1988/89 to 1996/1997 (Yasunaga et al., 2014b). These results suggested that levels of HCHs in the
Antarctic Ocean have varied from slightly decreasing to a steady state in the middle 1990s.
Isoda et al. (2014) summarized marine debris records in the JARPA and JARPA II for the period 1987/88
to 2010/11. In sighting records of research vessels, buoys made of plastic were the most abundant debris (69% of
all marine debris recorded) in Areas III East, IV, V and VI West. The highest density was recorded in Area V (DI:0.15) (DI: number of marine debris observed per 100 n. miles), followed by Area IV (DI: 0.12). DI of buoys in
Area IV and V suddenly increased after the 2005/06 austral summer season. The increase of buoy debris coincides
with an increase of long-line fishery operations in this area. The stomachs of a total of 10,041 Antarctic minke
whales was examined for debris. The number of occurrence of marine debris and objects other than prey in thefore and main stomachs per 100 Antarctic minke whales examined was estimated at 0.35. Four cases ofentanglement in a total of 10,041 Antarctic minke whales examined were found. Those involved fishing hook,monofilament fishing line, rope and packing band.
SPECIES INTERACTIONS
It has been documented that killer whales feed on Antarctic minke whales (see Pitman and Ensor, 2003 for review).
Likely effect of predation by killer whales on change of abundance estimates of Antarctic minke whales between
CPII and CPIII were considered but it was concluded that predation by killer whales didn't have considerable effecton Antarctic minke whales given estimated consumption rates of killer whales and their estimated abundance(Branch and Williams, 2003) although the relationship in the Indo-Pacific sector was not investigated explicitly.
Interaction with other baleen whales was suspected because number of baleen whales other than Antarctic
minke whales in this sector was increased from 1978/79 to 2008/09 as indicated by IDCR/SOWER and
JARPA/JARPAII estimates (e.g. Branch 2007; 2010; Hakamada and Matsuoka, 2014; Matsuoka and Hakamada2014). the population size of breeding stock D (BSD) of humpback whales which utilizes Area IV as there feeding
ground reached 90% of pre-exploitation in 2012 (IWC, 2015b). Changes in spatial distribution of large baleen
whales (southern right, fin, sei and humpback whales) in relation to Antarctic minke whales were examined in
Area IV and V-W using data obtained in IDCR/SOWER CPII and CPIII (Murase et al., 2011). Change in spatial
distribution of humpback whales in relation to Antarctic minke whales was examined in Area IV using
JARPA/JARPAII data (Murase et al., 2014). These studies revealed that spatial distribution of large whales
expanded as their number increased while that of Antarctic shrank toward south although improvement of thesemodels were recommended before making the conclusion (e.g. IWC, 2015a).
Attempts were made to construct multi-species ecosystem models in the period of the in-depth assessment.
Mori and Butterworth (2004) constructed a multi-species production model to model interactions among krill,
Antarctic minke and blue whales at a circumpolar scale. Additional species (fin and humpback whales, andAntarctic fur and crabeater seals) were included in their expanded model (Mori and Butterworth, 2006). There
were two modelled areas (the Indian and Atlantic sector consisted of Area II, III and IV, and the Pacific sector
consisted of I, V and IV) in the expanded model. Further refinement was attempted by Kitakado et al. (2014)
targeting Area IV though the results was preliminary at the time of the presentation. A preliminary attempt tomodel ecosystem of Area was also made by using a whole-ecosystem model, Ecopath (Kitakado et al., 2014).
SUMMARY OF CONCLUSIONS, FUTURE WORK AND DISCUSSION
After many years of working towards an in-depth assessment of Antarctic minke whales, we have now reached apoint where we can summarize what has been achieved, provide conclusions and determine what outstanding
issues are feasible and/or worthwhile to address in the future. This exercise started out trying to understand theabundance trends during observed in abundance surveys conducted during 1984 – 2004, but expanded to increasing
our knowledge on the population dynamics, biological characteristics (stock structure, abundance estimates, …)

15

and environmental interactions of the Antarctic minke whale. Conclusions from each of these types of data will besummarized and outstanding issues to be addressed in the future will be discussed.
Population dynamics
Results from VPA and SCAA population dynamic analyses were used to provide an overall assessment of thestatus of the Antarctic minke whales. The SCAA analysis was refined to account for more issues than could be
dealt with in the VPA framework; thus the SCAA has provided the final results of the assessment. Both of theseanalytical techniques required information on population structure, the degree of mixing among the stocks, agespecific catches from the commercial and research programs, and a series of abundance estimates. The most recent
version of the SCAA was also sex-specific, modelled multiple stocks simultaneously allowing some factors to beshared by both stocks, allowed for multiple catch fleets and regions, allowed for estimates of absolute and relative
abundance, and allowed for in each region changes over time in respect to carrying capacity, deviation in calf
numbers about the expected number of births, growth curves, sex- and length-specific vulnerability, and the
proportion of each stock in each region.
As a result of the 10-year development, the SCAA analysis in one of the most complex stock assessments
for any cetacean population. It finally focused on directly estimating changes over time in carrying capacity andrecruitment, and over age in natural mortality, and hence explored the possibility that the decline in recruitment isrelated to competition and other population dynamic-related factors. Given that carrying capacity for minke whales
was estimated to have changed over time, usual assessment measures such as population size relative to the(current) carrying capacity were not immediately straightforward to interpret. However, the results of the SCAA
model can be interpreted in the context of trends in abundance. The model implied that the total 1+ population size
increased annually by 1.9% (SE 0.50) for stock I and 2.1% (SE 0.70) for stock P per annum between 1945 and1968. The number of 1+ animals was then estimated to have declined by 54% (stock I) and 35% (stock P) from1968 to 2001. In addition, natural mortality was able to be estimated for age groups. However MSYR for 1+
animals was not able to be reliably estimated, primarily because the model estimated the stock was close to carrying
capacity throughout the assessment period. The SCAA was also not able to definitive conclude on the reason(s)for the abundance decline. However, the results point to the possibility that carrying capacity changed over time.
Though which biological reflexion of carrying capacity (pregnancy rates, infant survival rates, or changes inmaturity) was related to the abundance change was not able to be distinguished. Reliability of conclusions fromthe SCAA as recorded in IWC (2014) are shown in Table 7
Remaining areas for future work on the SCAA include: (a) consideration of other stock structure hypothesis,
including a soft boundary in Areas IV-E and V-W, which depends on year and sex (Kitakado et al., 2014), (b)evaluating the performance of the SCAA estimation method using simulations, (c) application of the model tobroader range of areas within the Antarctic, and (d) analyzing the estimates of deviations in calf survival rate to
identify the likelihood of possibly causal mechanisms for the changes in recruitment over time.
Stock structure
An essential piece of information needed in any stock assessment is the stock structure and degree of mixing. The
Antarctic minke whale’s stock structure has been investigated using genetic studies (mitochondrial and nuclear
DNA) and non-genetic studies (morphology, catch and sighting distribution patterns, and patterns derived from
Discovery marks and ecological markers). The 1990 Comprehensive Assessment assumed there was a single stock.
After the 1990 Comprehensive Assessment, using samples collected from JARPA research catches that started in
1987/88, genetic and non-genetic analyses revelled the single stock scenario was incorrect in the feeding groundsof Management Areas III East – VI West. There appeared to be at least two stocks that were probably related withthe proposed breeding areas in the eastern Indian Ocean (I-stock) and western South Pacific (P-stock) with an areaof transition in the region around 150 – 165°E. Refining the laboratory using samples collected under JARPA(1987/88 – 2004/05) and JARPA II (2005/06 – 2013/14) confirmed the two stocks and showed more dispersal inmales than females and also some degree of annual variability. Refining the analytical methods by developing an
integrated approach using morphometric, microsatellite and mtDNA data suggested the transition area (softboundary) varied by year and sex and the soft boundary was from 100 – 130°E in Area IV East and in 130 – 165°E
in Area V West. Therefore, the structure of Antarctic minke whale in Areas III East-VI West appears to be more
complex than originally thought: at least two stocks with a wider mixing area, which change by year and sex.
Kato and Fujise (2000) conducted an analysis on morphometric variation among dwarf minke, Antarctic
minke and western North Pacific common minke whales using ANCOVA and CDA. Analysis revealed significant
difference in external body proportion and skull measurements among three forms of the minke whales.
Abundance and distribution
Another essential piece of information needed in any stock assessment is an estimate of abundance, or preferable
a time series of abundance estimates. The 1990 Comprehensive Assessment only had available one set of16

abundance estimates for each Management Area. In contrast, the SCAA assessment analysis was able to utilize
two sets of absolute estimates from the IWC/IDCR sighting dedicated line transect survey from the last two
circumpolar sets: 1985/86-1990/91 (the second circumpolar survey; CPII) and 1991/92-2003/04 (the third
circumpolar survey; CPIII) and relative estimates derived from the JARPA/JARPA II program from 1989/90 to
2008/09 that were in Areas III East to VI West (35°E – 145°W, south of 60°S). The first circumpolar survey (CPI)
was not able to be used because it was not possible to correct for the biases due to the non-standard survey design
and to correct for the lack of data to estimate g(0).
After 10 years of discussion, abundance estimates for the IWC/IDCR/SOWER CPII and CPIII and theirassociated additional variance (i.e., process error) were agreed upon in 2012. These estimates were the result ofdeveloping/improving three independent analysis methods [IM (integrated model developed by Cooke, SPLINTR(spatial line transect) developed by Bravington and Hedley, and OK (Okamura-Kitakado) developed by Okamura
and Kitakado] and evaluating the performance and robustness of different versions of these three analysis methods
using simulated datasets. The final agreed upon estimates were derived from the evolved OK method withadjustments from the SPLINTR method.
After 6 years of discussion, abundance estimates derived from the JARPA/JARPA II data were corrected
for the non-standard survey design, changing g(0), school size bias, un-surveyed areas, and use of multiple survey
vessels at different times within the season.
Though the SCAA only used the Antarctic minke whale sightings data, the CP series surveys alsocollected sightings data on all cetaceans and the programme also included the collection of biopsy, photoidentification, oceanographic and acoustic samples.
In addition, experiment cruises from 2004/05 to 2009/10 were devoted mostly to experiments to address
problems encountered with the analysis of previous cruises. The experiments included the Buckland-Turnock
survey method that estimates g(0) and accounts for responsive animal behavior, school size estimation experiments,and visual dive time experiments. These experiments were instrumental in developing the final agreed upon
abundance estimates for the minke whales.
Another type of experiment cruise were the cooperative surveys with ice breakers (Australian Aurora
Australis in 1999, US Nathaniel B. Palmer in 1994 and 2004, and Japanese Sirase in 2004/05), helicopter surveys
from the Sirase, and Australian fixed-wing aircraft in 2007/08 – 2009/10. The objective of these experiments wereto investigate the minke whales that were distributed in the sea ice and so were not available to be counted during
the IWC/IDCR/SOWER and JARPA/JARPA II abundance surveys. These cooperative surveys in the sea ice
revealed that considerable numbers of Antarctic minke whales. However, absolute numbers could not be obtained
for the previous time periods of the CPI – CPIII series of abundance surveys.
Remaining areas of future work related to abundance estimates includes conducting surveys in the sea ice
most practically via aerial surveys, if more complete abundance estimates are needed.
Biological information
The biological information used by the SCAA included the length, age and sex of the commercial and research
catch, age-length relationships and age reading error (the extent of bias and inter-reader variability among readers).
The age of minke whales were traditionally estimated by counting growth layers in the earplug (Lockyer
1984), though the method using the ratio of aspartic acid racemization in eye lens was also explored (Yasunaga et
al. 2014). A statistical model quantifying age reading error was developed by Kitakado et al. (2013).
Growth curves (length – age relationships) were derived for the I- and P-stocks using JARPA samples(Bando et al. 2006). Physical maturity was determined based on the examination of the vertebral epiphysis of 6th
dorsal vertebra stained by 0.25% toluidine blue-O solution and used to estimate the age at 50% physical maturity.
Sexual maturity was determined by male testis histological samples and presence of corpora luteum or albicans infemales. This resulted in an estimated asymptotic length of about 20cm larger in I-stock than P-stock, 8.63m and8.45m for I-stock and P-stock males and 9.17m and 8.93m for both stocks of females. The mean body length
increased rapidly until about 7 years old and stops at around 15 – 20 years old. The estimated age at 50% physicalmaturity of males were younger than female for both stocks and the body length at 50% physical maturity of Istock was larger than P-stock. The estimated body length at sexual maturity of I-stock animals was 10-20cm larger
than P-stock for both sexes. The mean age of sexual maturity estimated from transition phase in earplugs declined
from 14 years in 1940s to 7 years in 1960s and remained constant at 7-8 years till the 1990s cohorts. Using JARPA
samples, the proportion of pregnant in matured female was estimated as 92.9% and 85.4% for I-stock and P-stock,
respectively and there was no significant trend going into the JARPA II time period.

17Remaining areas of future work related to estimating biological parameters is (a) to examine past yearlychanges of biological parameters for each stock, (b) to examine whether there is an age-dependent changes forreproductive parameter and (c) to detect possible future changes.
Food habits and energetics
The Antarctic minke whale feeds mainly on Antarctic krill (Euphausia superba) in offshore waters (Kawamura,
1980; Bushuev, 1986; Ichii and Kato, 1991), and on ice krill (E. crystallorophias) on the coastal shelf along suchareas as the Ross Sea and Prydz Bay (Bushuev, 1986; Tamura and Konishi, 2009). The daily prey consumption
by the whales in each reproductive status group was estimated using energy-requirement and energy deposition.
The daily prey consumptions during feeding season values were equivalent to 2.9 and 2.7 % of body weight for
immature and mature male, and 3.7 and 4.0 % of body weight for immature and mature female, respectively. The
total prey consumptions per capita during feeding season were 7.5 and 16.4 tons for immature and mature male,and 12.5 and 39.1 tons for immature and mature female, respectively (Tamura and Konishi, 2009).
Remaining areas of future work involve investigating several uncertainties (e.g. allometric relationships,
body weight of whales, energy values of prey species, assimilation efficiency and length of feeding period) in theestimations of prey consumption associated to the relevant parameters that can be treated by Monte Carlosimulations. Recently, development of individual-based models of cetacean foraging are progressing using themulti-sensor movement tag and telemetry data (Friedlaender et al., 2008, 2011, 2016).
In the 18 years JARPA period, a regression analysis of the annual trend in energy storage in the Antarctic
minke whale showed that blubber thickness, girth and fat weight had been decreasing for this period. The decrease
per year is estimated at approximately 0.02 cm for mid-lateral blubber thickness and 17 kg for fat weight,
corresponding to 9% for both measurements over the 18-year period (Konishi et al., 2008). The ScientificCommittee agreed that a decline in blubber thickness and in fat weight that was statistically significant at the 5%
level had occurred in JARPA period (IWC, 2015b; Konishi and Walloe, 2015). Remaining areas of future work
include several body condition analyses for the Antarctic minke whales based on scientific permit research catches.
Environmental aspects
The Antarctic minke whale is part of a larger physical and biological changing ecosystem. Antarctic minke whales
are both predator and prey and they also have to share the ocean with other predators. All of these factors influence
their distribution and density patterns. As noted earlier, high density areas of minke whales were commonly foundat the sea ice edge especially in and around the Ross Sea, and the abundance of minke whales has declined sincethe 1970s. The large scale distribution patterns over all years appears to be at least partially related to the presenceof sea ice (Ichii, 1990; Kasamuatsu et al., 2000; Matsuoka et al., 2003; Murase et al., 2002) and other transition
zones such as the continental shelf break (Beekmans et al., 2010; Murase et al., submitted). These transition zones
are known to be regions that aggregate krill, the minke whale’s prey. At a smaller scale presence of krill has beenshown to be associated with minke whale distribution (Friedlaender et al., 2008; 2011; 2016). Though it has been
documented that killer whales feed on Antarctic minke whales (Pitman and Ensor, 2003) and the numbers of killer
whales are increasing in the Antarctic, it does not appear that killer whales have been a driving factor in the broad
scale declining abundance trend since the 1970s (IWC, 2015b). In contrast, since the 1970s, as the distribution andabundance patterns of minke whales shrank and concentrated in the south near the ice edge, the patterns of otherbaleen whales (southern right, blue, fin, and humpback whales) expanded (Branch 2007; 2010; Hakamada andMatsuoka 2014; Matsuoka and Hakamada 2014; Murase et al., 2014). Attempts to model the multi-species
relationships between krill, Antarctic minke whales and other whale species have been attempted using multispecies production models (Mori and Butterworth, 2004; 2006; Kitakado et al., 2014) and the whole-ecosystem
Ecopath model (Kitakado et al., 2014).
Effects of humans relative to the Antarctic minke whale have also been changing. Trends of pollutants
levels of PCBs, DDTs, HCHs, HCB, CHLs show different patterns depending on which part of the Antarctic theminke whale resides in. This reflects not only the Antarctic environment but also the more northern breeding areas
where the minke whales spend the rest of the year. Minke whales in Areas V and VI showed extremely slowly
decreasing or a steady state patterns during 1988/89 – 2005/05. In contrast, levels in minke whales from decreased
significantly (Yasunaga et al., 2006b; 2014b; 2015). In addition, accumulation patterns of hepatic mercury varied
over years differently for young minke whales (1 – 15 years old) as compared to older animals. This suggested
that food availability in the 2000s changed compared to the 1980s and 1990s (Honda et al., 1987; Fujise et al.,
1997; Watanabe et al., 1998, Honda et al., 2006; Yasunaga et al., 2006a, 2014a). The sighting surveys have also
documented the locations of marine debris since 1987/88. In general levels of marine debris were low, with plastic
buoys being the most common item (Isoda et al., 2014). The number of buoys in Area IV and V suddenly increased
after the 2005/06 austral summer season which coincides with the increase of long-line fishing. This same general
pattern was seen in the 10,041 Antarctic minke whale stomachs that were examined for debris.

18

ACKNOWLEDGEMENTS
The authors acknowledge that the scientists who involved in the prolonged discussion on this in-depth assessment.
It was not possible to complete it without devotion and scientific passion of them. Data used in the assessment
were accumulated through large scale surveys which required substantial amount of investment by the contracting
governments and the IWC. Logistical arrangement required for subsequent analyses were also provided by them.
The authors express their thanks to the contracting governments and the IWC who offered such opportunities. The
authors also express their thanks to the crews and researchers who participated in the surveys to collect data whichwere vital to the assessment.
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26

CONTRIBUTORS
Takeharu BandoThe Institute of Cetacean Research(Biological Information)

Takashi Hakamada
The Institute of Cetacean Research(Abundance)

Tatsuya Isoda
The Institute of Cetacean Research(Pollutants and Marine Debris)

Hidehiro Kato
Tokyo University of Marine Science and Technology(Systematics, Catch, Surveys, Biological Information)

Toshihide Kitakado
Tokyo University of Marine Science and Technology(Abundance, Population Dynamics, Species Interactions)

Kenji Konishi
The Institute of Cetacean Research(Food Habit and Energetics)

Koji Matsuoka
The Institute of Cetacean Research(Surveys, Abundance)

Hiroto Murase
National Research Institute of Far Seas Fisheries, Japan Fisheries Research and Education Agency (FRA)(Catch, Surveys, Abundance, Spatial Distribution, Species Interactions)

Hiroshi Okamura
National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency (FRA)(Abundance)

Debra Palka
NOAA Fisheries, Northeast Fisheries Science Center

27

(Introduction, Discussion and Conclusions)

Luis Pastene
The Institute of Cetacean Research(Systematics, Surveys, Stock Structure)

Andre Punt
School of Aquatic and Fishery Sciences, University of Washington(Population Dynamics)

Tsutomu Tamura
The Institute of Cetacean Research(Food habit and energetics)

Genta Yasunaga
The Institute of Cetacean Research(Pollutants and Marine Debris)

28

Table 1. Longitudinal ranges of six areas in the in the Antarctic for management of large whale established by the
IWC (Donovan, 1991).

Area
I
II
III
IV
V
VI

Longitudinal range
120°W-60°W
60°W-0°0°-70°E
70°E-130°E
130°E-170°W
170°W-120°W

29

Table 2. Longitudinal ranges of eastern and western strata of Areas III, IV, V and VI.

Area and stratum
III East
IV WestIV EastV WestV EastVI WestAbbreviation
III-E
IV-W
IV-E
V-W
V-E
VI-W

30

Longitudinal range
35°E-70°E
70°E-100°E
100°E-130°E
130°E-165°E
165°E-170°W
170°W-145°W

Table 3. Agreed abundance estimates for CPII and CPIII in each IWC Management Area (IWC, 2013b). CNB:
Common Northern Boundary. AV: Additional Variance. See text for explanation.
IWC Management Area
CP

II

III

CPIII:CPII

I

II

III

IV

V

VI

Total

Survey once

85,688130,083

93,21555,237300,21455,617720,054

CNB

84,978120,02586,804

51,241

285,559

49,885

678,493

CV internal

0.16

0.14

0.2

0.17

0.13

0.22

0.08

CV with AV

0.34

0.4

0.44

0.39

0.31

0.39

0.18

Survey once

38,93057,206

94,21959,677

183,91580,835

514,783

CNB

34,36958,382

68,97555,899180,183

72,059469,866

CV internal

0.2

0.19

0.15

0.34

0.11

0.14

0.09

CV with AV

0.39

0.38

0.35

0.49

0.36

0.37

0.18

0.4

0.49

0.79

1.09

0.63

1.44

0.69

31

Table 4. Agreed abundance estimates of Antarctic minke whales in Areas IIIE, V, V and VIW by using
IDCR/SOWER CPII and CPIII data (IWC, 2013b). These estimates were used as input data to the population
dynamics model.

CP

Year

Area

Area size(n.mile2)

Abundance

CV

II
II
II
II
II
III
III
III
III
III

1986
1986
1988
1989
1991
1995
1996
1997
2002
2004

V-E
V-W
III-E
IV
VI-W
III-E
VI-W
IV
V-W
V-E

553,315369,337
216,439
473,477
311,778
269,356
446,457
476,894
349,951
478,019

154,658
105,951
11,782
46,763
20,43834,65948,20655,87343,640136,457

0.19
0.16
0.44
0.17
0.27
0.24
0.18
0.34
0.14
0.13

32

Table 5. Abundance estimates of Antarctic minke whales applying approach in Hakamada et al. (2013) to JARPA
and JARPAII data during 1989/90 – 2008/09 with (a) and without (b) taking into account model error. Theseestimates were used as input data to the population dynamics model.

(a)
Area
III-E
IV
V
VI-E

Average
Estimate
18,75932,714
101,106
15,486

Minimum
Estimate
CV
4,478
0.911
15,0880.645
67,661
0.308
8,434
0.601

Maximum
EstimateCV48,5400.711
63,7940.509
151,072
0.326
27,790
0.507

(b)
Area
III-E
IV
V
VI-E

Average
Estimate
18,56932,474
114,550
15,603

Minimum
Estimate
CV
5,566
0.367
14,7390.570
69,771
0.228
7,530
0.226

33

Maximum
Estimate
CV
44,801
0.582
62,9790.334
170,6210.129
26,3640.218

Table 6. Summary of biological parameters of Antarctic minke whales estimated by stock (Bando et al. (2006)).

I-stock(AreaS III-E, IV and V-W)

Parameters

P-stock(Areas V-E and VI-W)

Lmov
Lm50%
tmov
tm50%

Male
7.29
5.3

Female
8.4
8.16
7.9
7.6

Male
7.17
5.4

Female
8.3
7.97
8.4
8.0

Body length
at physical maturity (m)

50% mature

8.32

9.12

8.22

8.73

Age atphysical maturity

50% mature

16.0

21.2

17.0

20.6

Growthcurve

y=8.61(1-e-(0.27x+0.54))

y=9.16(1-e-(0.23x+0.49))

y=8.45(1-e-(0.29x+0.51))

y=8.93(1-e-(0.21x+0.59))

Proportion of pregnant
in matured female (%)

-

92.9

-

85.4

Foetal sex
ratio (male %)

-

51.8

-

46.8

Litter
size

-

1.007

-

1.013

Body length atsexual maturity (m)Age atsexual maturity

34

Table 7. Reliability of conclusions from the SCAA (Punt, 2014) as recorded in IWC (2014).

Model output

Historical trends in abundance

Extent of change from CPII to CPIII
MSYR
M (natural mortality)
Growth curves
Stock identity
Errors in age determination
JARPA/JARPA II abundance estimates
JARPA/JARPA II selectivity

Conclusion
Relative trends generally consistent – modelled
through changes in carrying capacity over about four
decades, with abundance peaking in around 1970.
The early and peak abundances are not quantitatively
reliable. Recent abundance fitted to CPII and CPIII
estimates.
Trends in abundance over the most recent 20 years
are relatively flat. Differences can be explained as
variability in distribution.
Not robust.
Weakly different by stock. CVs unrealistically low.
Further investigation recommended.
Not reliable – a proxy for some unmodeled source ofvariation.
An input; variable spatial distribution used toaccount for variability in abundance estimates.
Further exploration needed.
Important to take into account.
Biased low
Younger animals under-represented.

35

Fig. 1. Phylogeneny of mtDNA haplotypes of minke whale worldwide (modified version of Pastene et al., 2010)36

Fig. 2. Lateral view showing flipper and dorsal fin (top); view of the head (bottom) of B. bonaerensis

37

Fig 3. Number of Antarctic minke whales taken by commercial and scientific expeditions in the Indo-Pacificsector of the Antarctic (35°E-145°W) from 1956 to 2014. Lost individuals are included in the number. UK
caught 3 individuals from 1956/57 to 1959/60 but they are virtually invisible in this figure. Catch data wereextracted from Allison (2016).

38

Fig. 4. Catch positions of Antarctic minke whales (pink circles) by commercial and scientific expeditions in the
Indo-Pacific sector of the Antarctic (35°E-145°W) from 1956 to 2014. Catch data were extracted from Allison(2016). Bathymetry (Amante and Eakins, 2009) is also shown for illustrative purpose.

39Fig. 5. Estimated spatial distribution (expressed in number of individuals) of Antarctic minke whales in the IndoPacific sector of CPII (top) and CPIII (bottom; redrawn from Murase et al., submitted). Sighting effort (blacklines) and sighting positions of Antarctic minke whales (pink circles) are overlaid on the estimated spatial
distribution map. Sea ice concentrations at the time of the surveys (from Murase et al., 2011) are also shown.
Note that because satellite sea ice data is not available between 30°E and 70°E in CPII, the area is masked inblack.

40

Fig. 6. Possible correspondence between areas of high sighting densities in the eastern Indian Ocean and westernSouth Pacific (Kasamatsu et al., 1995) and feeding areas in the Antarctic (from Pastene and Goto, 2016) (see text
for details).

41

Fig. 7. Current hypothesis on stock structure of the Antarctic minke whale. At least two stocks occur in the researcharea of JARPA/JARPAII, which mix through a transition area, which appear to change by year and sex.

42

Fig. 8. Bootstrap estimates of total (1+) population size and recruitment for minke whales in
Antarctic Areas IV and V based on the VPA, shown as medians together with estimates of 95%
CI’s.(recomputed from Butterworth . 2002)

43

Fig. 9. Estimates of total (1+) population size and recruitment for Stock I and P minke whales
based on the SCAA. The dashed lines indicate 95% asymptotic confidence intervals

44

Fig. 10. Estimates of natural mortality for Stock I and P minke whales based on the SCAA. The
dashed lines indicate 95% asymptotic confidence intervals. The horizontal dotted lines are theestimates of natural mortality from the VPA for Antarctic Areas IV and V.

45