SC/F14/J03

Estimates of abundance and abundance trend of the
Antarctic minke whale in Areas IIIE-VIW, south of
60ºS, based on JARPA and JARPAII sighting data
(1989/90-2008/09)
TAKASHI HAKAMADA AND KOJI MATSUOKA
Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055, Japan

Contact e-mail:hakamada@cetacean.jp
ABSTRACTThe Japanese Whale Research Program under Special Permit in the Antarctic (JARPA) and its second phase (JARPA II) conducted
sighting surveys during the 1989/90 to 2008/09 austral summer seasons (mainly in January and February), alternating between
western and eastern sectors of the research areas, both south of 60°S in each year. These data are analyzed to obtain abundance
estimates for Antarctic minke whales (Balaenoptera bonaerensis) in Areas IIIE-VIW. The estimates are calculated by standard linetransect analysis methods using the program DISTANCE under the assumption that g(0)=1. Annual rates of increase in abundance
are estimated using log-linear models. The analyses take several recommendations from the 2006 JARPA Review Meeting into
consideration. Log-linear models are used to adjust for different strata being surveyed at different times of year over the duration of
JARPA and JARPAII, with model selection being based on AICc. Abundance estimates for each Area during the JARPA and
JARPAII period were as follow:

Area

Average
Estimate

Minimum
Estimate
CV

Maximum
Estimate
CV

IIIE
IV

18,56932,474

5,566
14,739

0.367
0.570

44,801
62,979

0.582
0.334

V
VIW

114,550
15,603

69,771
7,530

0.228
0.226

170,621
26,364

0.129
0.218

The estimates that took into account the model error were as follow:

Area

Average
Estimate

Minimum
Estimate
CV

Maximum
Estimate
CV

IIIE
IV

18,75932,714

4,478
15,088

0.911
0.645

48,54063,794

0.711
0.509

V
VIW

101,106
15,486

67,661
8,434

0.308
0.601

151,072
27,790

0.326
0.507

For the estimates that took the model error into consideration the annual rates of increase in abundance were 1.1% with a 95% CI of[-2.3%, 4.5%] for Area IIIE+IV and 0.6% with a 95% CI of [-2.2%, 3.3%] for Area V+VIW. Estimates of these trends were robust
to the effects of changes in survey timing, the shapes of the shoulders of detection functions, portions of survey tracklines following
the ice edge, parts of the Areas in which no survey took place and poor coverage within some strata. Adjustments to allow for theg(0) being less than 1 were made by the application of a regression model, developed from the results of the Okamura-Kitakado(OK) method estimate of Antarctic minke whale abundance from the IDCR-SOWER surveys, which provided estimates of g(0)from the statistics of the Antarctic minke whale school size distribution in a stratum. With this adjustment, abundance estimates
increased by an average of 23,984 (88%) for Area IV and 105,906 (109%) for Area V, while the estimates of annual rates of increase
and their 95% CIs changed to 2.5% [-1.3%, 6.3%] for Areas IIIE+IV and -0.6 % [-3.9%, 2.6%] for Areas V+VIW.

KEYWORDS: ANTARCTIC; ANTARCTIC MINKE WHALE; SIGHTING SURVEY; SURVEY-VESSEL;ABUNDANCE ESTIMATE; TRENDINTRODUCTION
Based on sighting data collected during the International Whaling Commission Scientific Committee(IWC SC)’s International Decade of Cetacean Research (IDCR) (Matsuoka et al., 2003) from 1982/83 to
1988/89 (the second circumpolar set of surveys – CPII), the circumpolar abundance of Antarctic minke
whales south of 60ºS was estimated at 761,000 (IWC, 1991) under the assumption that all schools on the1

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trackline are seen (g(0)=1). Subsequently, the methodology used in 1990 has been refined in a number ofways, in particular to make use of models which allow for the possibility that g(0)<1 (Okamura andKitakado, 2012; Bravington and Hedley, 2012). Using results from these approaches, the so-called OK
and SPLINTR approaches, the IWC SC subsequently agreed that 720,000 for CPII (1985/86-1990/91)
and 515,000 for CPIII (1991/92-2003/04) represent the best available abundance estimates of the
Antarctic minke whales in the areas surveyed during the IDCR and Southern Ocean Whale andEcosystem Research (SOWER) programmes (IWC, 2013).
The main objective of this paper is to produce revised estimates of abundance and trends of Antarctic
minke whales based on the JARPA and JARPAII sighting data which take into consideration therecommendations from the JARPA review meeting (JRM) conducted by the IWC SC in 2006 (see IWC
2008a pp349). Approaches in Hakamada et al (in press) are applied for the estimates.
MATERIALS AND METHODS
Details for the survey procedure during JARPA were explained in Hakamada et al (in press).
Sighting survey procedure during JARPAII
Survey area and geographical stratification
The main sectors for the full scale research that are surveyed in alternate years were Areas IIIE, IV and V(35oE - 175oE) and Area V and VIW (130oE - 145oW), south of 60ºS; each of these Areas was divided into
smaller strata (Figure 1). Specifications of the stratification are given in Figure 1. Distributions of the
primary sightings of minke whales and of efforts in Areas IIIE, IV, V and VIW for each year are shown in
Figure 2a-2d.
Monthly coverage and order of the surveys
The JARPAII research period ranged from the end of December to March in each year, regular research in
Areas IV and V was concentrated in January and February in most years, which coincides with the peak
period for migration of minke whales to their Antarctic feeding grounds (Kasamatsu et al., 1996). The
order in which the strata were surveyed within the research period (December-March) each year is shownin Figure 3 for both Areas. Start and end dates in JARPAII surveys are shown in Figure 4. The end date
was earlier than usual in 2006/07 due to a fire accident on the research base Nisshin-Maru (Nishiwaki etal., 2007). Abundance estimates are based on single coverage of the blocks shown in Figure 1 in the year
concerned.
Trackline design
The trackline was designed to cover the whole research area and was followed consistently throughout the
JARPA and JARPAII surveys (Figure 2a-2d). The starting points of the trackline were selected at random
from 1 n.mile intervals on lines of longitude. Trackline way points (where the trackline changes direction)
were systematically allocated on the ice edge and on the locus of points 45 n.miles from that edge insouthern strata, and on this locus and the 60oS latitude line in the northern strata. There were two
modifications in trackline design in JARPAII surveys considering the recommendations at the JRM to
improve abundance estimation. One is that the saw-tooth type trackline for the southern strata was chosen
to allow for wide area coverage in JARPA but was not chosen in JARPAII. Another is that northern andsouthern strata were surveyed in the same period (Nishiwaki et al., 2014) to avoid that temporal gaps
occurring in the survey period of southern and northern strata during JARPAII period.
Sampling and Sighting Vessels (SSVs) and Sighting Vessels (SVs)JARPAII comprised a combination of sighting and sampling surveys. SSVs and SVs were surveyed
independently. Researchers search for schools until a school is detected, and then proceed to confirm its
species and school size. The procedure they use is identical to that of a SV in closing mode (Nishiwaki etal., 2006), except that once this confirmation has been achieved SSVs attempt to catch minke whales
targeted within the school in terms of specified procedures (Nishiwaki et al., 2006). During the JARPAII
period, SSVs covered south of 62oS whereas SVs covered south of 60oS. Therefore sighting data obtainedby SSVs were not used for abundance estimation.
Closing and passing mode
Fundamentally, the survey protocols of JARPAII follow those of IDCR (Nishiwaki et al., 2014). A SV

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surveyed in passing mode (SVP) for the first 8 hours of the day and in closing mode (SVC) during therest of the day. Therefore, the allocation of effort between passing mode and closing mode was systematic.
By comparing abundance estimates for SSV and SVC modes to SVP (which in principle gives less biased
results because it avoids the “end effects” introduced by closure), the effect of survey mode on theabundance estimates can be examined.
Unsurveyed area
Some small parts of Area IV were not surveyed on four of the cruises in the JARPA period. These
geographical “gaps” arose because of the southward retreat of the ice edge after the survey of the more
northerly of the two strata concerned had been completed, necessitating re-location of the trackline for the
more southerly stratum. For more details on the “gaps”, see Hakamada et al. (in press). Because northernand southern strata were surveyed in the same period (Nishiwaki et al., 2014), such “gaps” did not occur
during the JARPAII period.
Due to violent action by an anti-whaling non governmental organisation in the research area, the SVs and
SSVs could not carry out the research in the planned track line in Area III East (35°E - 70°E), a part ofArea IV (90°E - 130°E) and a part of Area V West (130°E - 132°E) in 2009/10 (Nishiwaki et al., 2010).
Due to violent action by an anti-whaling non governmental organisation in the research area, a sighting
survey by SV was not conducted in 2010/11 (IWC, 2012). Therefore, abundance in these years could not
be estimated.
Tracklines following contours of the ice edge
At the IWC SC meeting in 2006, there was a discussion as to whether some lengthy intermediate transects
which run nearly parallel to the ice edge might introduce bias, particularly for design-based abundance
estimation approaches. For more details on this was provided in Hakamada et al. (in press). Having somesegments of the tracklines not parallel to lines of longitude could lead to an overestimate of abundance
because some of these segments run virtually along the ice edge in strata where saw-tooth shapetracklines designs were used. As mentioned above, saw-tooth type tracklines were not adopted in any
strata in the JARPA II period so that such tracklines rarely occurred.
Pre-determined daily distance coverage
A pre-determined distance for daily movement along the research track line was calculated so as to cover
the survey area within the schedule for JARPA from the 1989/90 to the 1992/93 seasons. Explanation of
the pre-determined distance was provided in Hakamada et al. (in press). Pre-determined daily distance
was not set during JARPA II period.
Analytical procedure
Before explaining the analytical procedures applied in this paper, it is useful to list the six fundamental
steps which these involve. The procedure is same as that in Hakamada et al. (in press) except that the step2) below was added in order to interpolate the abundance estimate to unsurveyed areas in the JARPA II
period.
1) Estimate the abundance in stratum for each survey mode using the “standard methodology” of Branch
and Butterworth (2001) for IDCR-SOWER line transect data.
2) Abundance estimated using approach using a log-linear model (Kitakado et al., 2012) for strata whichsurvey coverage was incomplete.
3) Conduct sensitivity tests to investigate whether different treatment of the sighting data might affect theabundance estimates obtained in step 1) substantially.
4). Apply a model selection criterion to choose amongst different log-linear models to examine the effectsof survey mode and survey timing, and to estimate abundance trends.
5) Examine the sensitivity of estimates of abundance trends by analyzing the abundances estimated instep 3.
6). Estimate corrected abundances using the correction factors for the survey mode effect estimated instep 4).
Note that abundances were estimated by survey mode because it has been suggested that these may differ
depending on the survey mode (Haw, 1991).

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Smearing
The data recorded for radial distance and angle are smeared using the method II of Buckland andAnganuzzi (1988). The smearing parameter values used in this study are shown in Table 1. After thesmearing, the perpendicular distances are truncated at 1.5 n.miles. This treatment is the same as the one
employed in abundance estimation from the IWC IDCR-SOWER data (Branch and Butterworth, 2001;
Branch, 2006). The number of sightings remaining after smearing and truncation includes sightings withboth confirmed and unconfirmed school sizes.
Correction of observed angle and distance
To be able to correct for biases in distance and angle estimation, a distance and angle estimation
experiment was conducted on each vessel each year (Nishiwaki et al., 2006). The correction factors
estimated for observed angles and distances for each vessel are listed in Table 1 of Hakamada andMatsuoka (2014) have been used for these analyses. More details of the methodology for estimation ofthese correction factors may be found in Branch and Butterworth (2001).
Abundance estimation
The methodology for abundance estimation used in this study is described by Branch and Butterworth(2001) and Branch (2006), and has been termed the “standard methodology” in the IWC SC. The program
DISTANCE ver 6.0 (Thomas et al., 2010) was used to provide abundance estimates corresponding toeach track line1. The following equation was used for abundance estimation in each stratum:

AE ( s )ni
,
2 wLi

Pi =

(1)

where
Pi is the abundance in numbers as estimated from the i th trackline,
A is the open ocean area of the stratum,
E(s) is the estimated mean school size,n i is the numbers of primary sightings of schools on the i th trackline,
w is the effective search half-width for schools and
L i is the primary search effort on the i th trackline.
For SSVs, the total abundance in each stratum is calculated as:

∑L P
i

P=

i

i

.

L

(2).

where L is sum of the Li for each of the SSVs in the stratum.
The CV of the total abundance estimate P, is then calculated for each stratum using the equation:2

  n CV( P) = CV  + {CV( E ( s ))}2 + {CV( w)}2 ,  L (3)

where n is the sum of ni for all the SSVs. Estimation of the CV of n/L is as specified in equation (5)below.
Detection function
A hazard rate model with no adjustment terms was used for the detection function:  y  −b f ( y ) = 1 − exp−     a  

(4)

where y is perpendicular distance, and a>0 and b≥1 are parameters of the model to be estimated. It is
assumed here that g(0)=1 (i.e. the detection probability of a school on the track line is 1). Detections with1

The reason why “track line” was used rather than “vessel” here is because the location of each SSV among the twoor three parallel tracklines was changed each day (see Appendix 2 of Hakamada et al, (in press)).

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perpendicular distances of more than 1.5 n.mile were truncated when estimating effective searchhalf-width (ESW) w. More details of this detection function are given in Buckland et al. (1993; 2001).
Stratification of data to estimate ESHW (effective search half width)
In line with the IWC (2008a) recommendation AE9, ESHWs were estimated by stratum. In cases where
the sample size was smaller than 15, the sighting data were pooled among strata to estimate the detection
function in line with the the IWC (2008a) recomm. AE5. In such cases, data were pooled across West-East
strata because sighting conditions and school size distributions are expected to be more similar than for
North-South strata. In instances where there were less than 15 detections in southern/northern strata, data
were aggregated over the whole of each Area.
Estimated mean school size
Again in line with the IWC (2008a) recommendation AE9, mean school sizes were estimated by stratum.
Only the primary sightings for which the school size was confirmed were used for the estimation. The
method for estimation of the mean school size described in Buckland et al. (1993; 2001) was used. Morespecifically, regressions of the log of observed school size against f(y) was conducted for this purpose. If
the regression coefficient was not significant at the 15% level, the observed mean school size forsightings within a distance of 1.5 n.miles was substituted instead in the equation (1). If the consequentmean school size estimated was less than 1, then the observed mean school size was substituted instead in
the equation (1) even if the regression coefficient was statistically significant at this 15% level. Similarly
to the analyses for the IDCR-SOWER data (Branch and Butterworth, 2001; Branch 2006), for SVP themean school size estimated from SVC data was used instead of estimating this from SVP data, for which
school size estimates are known to be negatively biased as a result of not approaching all schools closely(Butterworth and McQuaid, 1986).
Combined encounter rate taking account of correlation among two or three SSV track lines (IWCb, 2008arecommendation AE6)
The survey by the SSVs comprised two or three parallel tracklines. There may be a positive correlation in
the encounter rates along these lines, which would cause a negative bias in the estimate of the CV of theoverall encounter rate if the results from each vessel were assumed to be independent. To take thispossible covariance into account, the CV of the encounter rate when combined over the two or three
SSVs with their parallel tracklines was estimated as:  ni var  ni 
  L i CV  =ni
 Li 
Li
where

(5)

ni = ∑ ni , j Li = ∑ Li , jjjwith ni,j and Li,j being the number of primary sightings of minke whale schools and the primary effort on
the i th transect as surveyed on the j th tracklines,. The variance of (n/L) is calculated as:
2

1  Li   ni n n kVar   = ∑   − 
 L  i =1 (k − 1)  L   Li L 

2

(6)

where k is the number of transects on each trackline.
Log-linear models to estimate abundance trend considering the effect of survey times
In order to examine the effect of survey timing, the four models shown below were considered.
Model i): log( Pobs ( y, a)) = log(Ptrue (0, a)) + αy + ε y ,a + η y ,a ,(7)Model ii):log( Pobs ( y, a)) = log( Ptrue (0, a )) + αy + M + ε y ,a + η y ,a ,(8)Model iii):

log(Pobs ( y, a)) = log(Ptrue (0, a)) + αy + M + T + ε y ,a + η y ,a ,(9)Model iv): log( Pobs ( y, a )) = log( Ptrue (0, a )) + αy + M + T + a ∗ T + ε y ,a + η y ,a ,

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(10)

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where
y is the year,
a is the stratum,
Pobs(y,a) is the observed abundance estimate in stratum a and in year y as obtained from the line transect
analyses,Ptrue(y,a)is the underlying abundance (i.e. free from the effect of survey mode) which is to be estimated inyear y and in stratum a,
M is the survey mode factor,
T is the categorical variable related to survey time as defined below,
a*T is an interaction between strata and survey timing,
ε y,a is an error reflecting the sampling error of the survey abundance estimate in year y and stratum aandη y,a is a normally distributed error with mean of 0 and variance of σ associated with “model error”.
2

To assess sensitivity, models that replace abundance estimates with density estimates were also evaluatedto estimate abundance trends.
The middle day of the survey period in each stratum was calculated and categorized into groups as a basis
to specify T. Because the estimate of trend α might be sensitive to the definition of T, four grouping wereconsidered:
1) T=1: Dec 1-Jan 15, T=2: Jan 16-31, T=3: Feb 1-15, T=4 Feb 16-Mar15 (Grouping T1)
2) T= 1: Dec 1-Jan 15, T=2: Jan 16-Feb 15 and T=3: Feb 16-Mar 15 (Grouping T2)
3) T= 1: Dec, T=2: Jan, T=3: Feb and T=4: Mar (Grouping T3)
4) T= 1: Dec and Jan and T=2: Feb and Mar (Grouping T4)
The groups in bold letters were included in the intercept of the alternative models considered (i.e. theeffect of those groups is set to zero in the calculations). T1 – T4 were used as categorical covariates inModels iii) and iv) (equations (9) and (10)) above. The best grouping was selected by comparing the
Akaike Information Criterion (AIC) (Akaike, 1973) for each model.
Log likelihoods for these models are provided by,

{

}

−1

log det(V + σ I )( x − xˆ )(V + σ I ) ( x − xˆ ) nLL(θ , σ 2 ) =  −  log(2π ) −
−
2
2
 2
2

t

2

(11)

where
θ is the vector of parameters to be estimated,
n is the number of data available to fit the model,
V is the variance-covariance matrix of the abundance estimates,
I is the identity matrix,
X is a vector of log of the observed abundance (density) estimates,x̂ is a vector of log of predicted abundance (density) by one of the models i) to iv) (equations (7)-(10))
above andt indicates transpose of the vector
The variance-covariance matrices for the estimated parameters V ( θ ) were derived from the Information
matrix. It should be noted that “additional variance” as estimated by maximum likelihood (ML) using
equation (11) is negatively biased
In cases where sample size is small or the number of parameters is large compared to the sample size, it is
known that AIC over- estimates the appropriate number of parameters to be estimated (i.e. AIC has
tendency to select a model that is too complex). Because there may be many such parameters whencompared to samples size for Model iv), an adjusted value, corrected AIC (AICc) (Sugiura, 1978; Hurvich
and Tsai, 1989; 1991), which can be applied to linear models with normal errors, is used instead of AIC.
As AICc and AIC are asymptotically equivalent, AICc can also be applied in cases of large sample size.
AICc is defined by:

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2( p + 1)( p + 2)
AIC C = 2 log( LL (θˆ, σˆ )) + 2( p + 1) = AIC +n− p−2(12)

where (θˆ, σˆ ) are the parameter values that maximize the log-likelihood, p is the number of parameters
estimated in the model concerned. On implementation of models (7) – (10), nominal abundance estimatesin Areas IIIE and IV together and those in Area V and VIW together.
Correction of nominal abundance estimates and their variance-covariance matrices
When model i) was selected under AICc, the observed abundance estimates from the line transect analyses
can be utilised as they stand to calculate estimates averaged over survey modes in the manner described
below. Their variance-covariance matrices were derived from a bootstrap approach using equation (13)below. When one of the models ii) to iv) was selected however, the nominal abundances from the linetransect analyses first needed to be adjusted using factors estimated from the model selected. If models
that include the survey time covariate were selected, the nominal abundance estimates were adjusted tocorrespond to January. This provides total abundance estimates for Areas IIIE, IV, V and VIW for eachsurvey mode. To estimate variance and covariance for these adjusted abundances, resampling techniques
were used rather than to attempt to estimate them analytically, because the correlation between adjustment
factors and the nominal abundance estimates made the latter approach difficult. A total of 1000 resamples
of abundance were generated for each stratum using a parametric bootstrap approach, and parameters
estimated for the selected model to in turn estimate the variance-covariance matrix, as follows:(x pseudo ~ N x ,V + σ 2 I

)

(13)

(θ , σ ) psudo ~ N ((θ , σ , Σ (θ ,σ ) )(14)

where Σ (θ ,σ ) is variance-covariance matrix of the estimated parameters of models i) to iv).
Weighted average of abundance over survey modes
Weighted averages of abundance estimates over survey modes were calculated, where the weights werechosen to minimize the associated variances. Thus the weighted average Pwa and its variance V(Pwa) wereobtained from:
PWA = wPSSV + (1 − w) PSVC
(15)

V ( PWA ) = w 2V ( PSSV ) + 2 w(1 − w)Cov ( PSSV , PSVC ) + (1 − w) 2 V ( PSVC )

(16)

where
w=0 if V ( PSVC ) ≤ Cov( PSSV , PSVC )w=1 if V ( PSSV ) ≤ Cov ( PSSV , PSVC )w=

V ( PSVC ) − Cov ( PSSV , PSVC )V ( PSSV ) − 2Cov ( PSSV , PSVC ) + V ( PSVC )otherwisefor 1992/93-1996/97 andaPSSV + bPSVC + cPSVP
(17)
a+b+c
a 2V ( PSSV ) + b 2V ( PSVC ) + c 2V ( PSVP ) + 2{abCov( PSSV , PSVC ) + bcCov( PSVC , PSVP ) + caCov( PSSV , PSVP )}V ( PWA ) =(a + b + c )2
PWA =

(18)
where

a = {V ( PSVC ) − Cov ( PSSV , PSVC )}{V ( PSVP ) − Cov ( PSSV , PSVP )}− {Cov ( PSVC , PSVP ) − Cov ( PSSV , PSVC )}{Cov ( PSVC , PSVP ) − Cov ( PSSV , PSVP )}b = {V ( PSSV ) − Cov( PSSV , PSVC )}{V ( PSVP ) − Cov( PSVC , PSVP )}− {Cov( PSSV , PSVP ) − Cov( PSSV , PSVC )}{Cov( PSSV , PSVP ) − Cov( PSVC , PSVP )}7

SC/F14/J3

c = {V ( PSSV ) − Cov ( PSSV , PSVP )}{V ( PSVC ) − Cov ( PSVC , PSVP )}− {Cov ( PSSV , PSVC ) − Cov ( PSVC , PSVP )}{Cov ( PSSV , PSVC ) − Cov ( PSSV , PSVP )}for 1997/98 – 2004/05. If any of a, b and c was negative, that value would be substituted by 0.
For 2005/06-2008/09, equations (15) and (16) replacing Pssv by Psvp were applied to estimate PWA and its
variance because there were no abundance estimates for SSV during the period.
Sensitivity tests for abundance and trend estimates
There are various possible sources of bias in abundance estimates and their trends, the more important of
which were discussed at JRM and at the IWCb SC meeting in 2007 (Table 2) (see IWC 2008a pp349). In
order to examine their possible magnitudes, sensitivity analyses were conducted.
The effect of a’ shoulder’ in the detection functions
For SSVs, the detection function has a clear shoulder in most cases Hakamada et al. (in press). For cases
where b in equation (4) is estimated to be 1, however, the detection function would hardly show ashoulder. Estimates of ESHW in those strata were replaced by the average of ESHW for same strata inother years as in this test, which assumed that the CV of the estimated ESHW was same as before thereplacement.
For SVs, though some of the detection functions show a good fit to the data, others do not have a clear
shoulder, perhaps related to smaller samples sizes than are typical for the SSVs. In order to examine thedetection functions for the SVs further, the MCDS (Multiple Covariates Distance Sampling) module in
DISTANCE was used (Thomas et al., 2010). MCDS can incorporate covariates other than perpendiculardistance to estimate the scale parameters of detection functions. The data were stratified into Northern
strata, Southern strata, Prydz Bay, Area IIIE and Area VIW separately, because the ESHW estimate is
expected to differ in relation to distance northwards. AICc values were compared to select covariates toinclude in the detection function model below. The hazard rate function was utilized, with the full model
described by:
−b
 y


f ( y ) = 1 − exp− 
 
a
EW
year
+
exp(
)
 
 

(19)

where y is perpendicular distance, a>0 and b≥1 are estimable parameters, and EW and year are
categorical covariates for whether the stratum is to the East or the West, and for the year when the survey
was conducted, respectively.
Treatment of segments of tracklines following contours of the ice edge (IWC, 2008a recommendation AE
12)
To examine the effect of alternative treatments, analyses were conducted for the SE and SW strata in Area
IV and the SW stratum in Area V where saw-tooth tracklines designs were used during JARPA period.
Abundances were estimated excluding trackline segments that were essentially along the ice edge (OptionB), and also for exclusion of tracklines not parallel to lines of longitude (Option C). The results arecompared to the base case for which the complete tracklines are used. Saw-tooth tracklines were not usedduring JARPA II period, and therefore nominal abundance estimates to apply models (7)-(10) were sameas those used for the base case.
Unsurveyed areas (IWC, 2008a recommendation AE 10)
For the cases such “gaps” occurred during JARPA period, two approaches were pursued to attempt tobound the uncertainty associated with the treatment of “gaps” in coverage as defined above for the basecase estimates. On the conservative side, the abundance contributions from these gaps were set to zero (i.e.
whales in such gaps at the time of surveying the more southerly strata were considered to be ones alreadyeffectively counted in the earlier survey of the more northerly strata, as these whales would subsequently
likely have moved further south). On the liberal side, the density in a gap was assumed to be the same as
the higher density in the stratum immediately to the south, rather than that immediately to the north as in
the base case. No “gaps” occurred during the JARPA II period, and therefore nominal abundance
estimates to apply models (7)-(10) were same those used for the base case.

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Incomplete coverage (IWC, 2008a recommendation AE 11)
For the base case estimates of abundance, the interpolated density for the (virtually) unsurveyed portionof a stratum was taken to be the same as that in the surveyed portion. To check sensitivity to an alternative
to this assumption, the average of the ratio of the densities in these two portions of the stratum on surveys
in other years was evaluated, and this was used instead to extrapolate the density in the surveyed portion
to that for the (virtually) unsurveyed portion for the year in question. The development of such averages
did not include data from every other cruise, as consideration was also given to similarities of ice-edge
configurations amongst the cruises. For JARPA surveys, the strata for which such alternative
computations were conducted in Hakamada et al. (in press). Table 3 listed strata which considered
incomplete coverage occurred during 2005/06 – 2008/09 JARPA II surveys. For these areas, generalized
linear model (GLM) were applied to extrapolate abundance estimate as follows;

Log( Pobs ( y , i )) = Log( Ptrue ( y, i )) + M + τ i + ε i , y

(20)

where
y is the year,
i is the survey block,Pobs(y,i) is the observed abundance estimate in survey block i and in year y as obtained from the linetransect analyses,
Ptrue(y,i)is the underlying abundance (i.e. free from the effect of survey mode) which is to be estimated inyear y and in block i,
M is the survey mode factor,
ε i, y is an error reflecting the sampling error of the survey abundance estimate in year y and survey block i
and2
τ i is a normally distributed error with mean of 0 and variance of ρ associated with additional variance.
Survey block are defined in Areas IIIE, VW and VW, respectively as follows;
Area IIIE (4 blocks): (a part of IIIEN stratum west of 55oE), NW2 (a part of NW stratum east of 161oE),
SW1 (a part of SW stratum west of 159oE) and SW2 (a part of SW stratum east of 159oE)
Area IVW (3 blocks): NW, SW and PB (Same as strata shown in Figure 1).
Area VW (4 blocks): NW1 (a part of NW stratum west of 161oE), NW2 (a part of NW stratum east of
161oE), SW1 (a part of SW stratum west of 159oE) and SW2 (a part of SW stratum east of 159oE)
Table 4 shows which block were surveyed or unsurveyed in each year. Abundance estimates wereinterpolated abundance estimate for strata including unsurveyed blocks by applying equation (20).
The effect of “skipping” to cover the pre-determined daily distance
The approach of specifying a pre-determined distance for travel each day was discontinued after 1992/93.
In order to eliminate any impact that possible consequent biases in the associated estimates of abundancemight have had on the estimated abundance trend, estimates prior to the 1993/94 survey were omitted
when implementing models i) to iv).
Sensitivity of abundance trend
Models i) to iv) were applied to abundance estimates obtained in the sensitivity tests above under theassumption that the variance-covariance matrix for the abundances was same as that for the base case.
Again, the best model was selected using AICc.
Adjustment for g(0) less than 1
The Okamura and Kitakado (2012) and the Bravington and Hedley (2012) approaches (known
respectively as the “OK” and “SPLINTR” methods) for estimating minke whale abundance from the
IDCR-SOWER data resulted in estimates of g(0) which were less than 1 for minke whales, especially sofor schools of size 1. Furthermore, since school sizes tended to be less for later surveys, it was importantto take this into account when estimating the extent of possible changes in minke whale abundance over

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time. Because neither the SSVs nor the SVs participating in the JARPA and JARPA II surveys had
Independent Observer Platforms, whose observations are needed to identify the duplicate sightings uponwhich the OK and SPLINTR methods rely, they cannot be applied directly here.
Instead, noting that the key (albeit not exclusive) dependence of the g(0) estimates from these methods onschool size, a regression approach conducted in Hakamada et al. (in press), was updated. This uses theestimates of g(0) provided by the OK approach for each survey block in their analyses to fit to a linear
model whose covariates include the mean and standard deviation of the school size for each block,
together with other factors readily available such as (Management) Area and whether the block concerned
adjoined the ice-edge (S) or the northern boundary (N) of the survey (these co-variates are serving as
proxies for the environmental conditions, such as Beaufort sea state, taken into account in the OK
analyses). As the JARPA and JARPA II surveys did not include Independent Observers as in IO mode for
the IDCR-SOWER surveys, for comparability the OK estimates from those latter surveys which are usedhere are for closing mode survey which does not have Independent Observers involved in searching forwhales. The school size distribution statistics were based on primary sightings in closing mode whose
school size had been confirmed; furthermore to curb undue influence of outlier values arising from theoccasional very large school, means above 8 were treated as equal to 8, and similarly standard deviations
above 4 were treated as equal to 4. Because the resulting equation was to be applied JARPA and JARPA II
results for Areas III, IV, V and VI only OK estimates of g(0) for these four Areas (a total of 43 estimates)
were used in the regression. A number of models were investigated, considering also interactions amongst
the factor mentioned above and whether to treat the two Areas separately or jointly; these possibilities
included introducing quadratic terms in mean school size and its standard derivation. On the basis of AICc,
the following model was selected:g(0)= a + b.E(s) +c.sd(s)+NS +Area+NS*Area(21)

where a, b and c are parameters estimated in the model fit, sd(s) is the standard deviation of the school
size distribution for the block, and NS, Area and their interactions are categorical variables.
Figure 5 compares the values of g(0) predicted by this model to the OK estimates; given that school sizes
tended to be smaller for the third circumpolar set of surveys (CPIII) than for the second, it is unsurprisingthat the CPIII points shown in this Figure tend to reflect higher values of g(0) than do the CPII points.
Table 5 lists the estimates of the parameter values of equation (21); given that the relationship between
g(0) and school size s must asymptote at g(0)=1 and therefore be non-linear with a negative second
derivative, the negative value obtained for the c parameter is to be expected, as larger values of sd(s)reflect the influence of a greater proportion of singleton schools.

RESULTS
Abundance estimates
Table 6a-6j show abundance estimates by strata in Areas IIIE and VI during JARPA and JARPA II and in
Areas IV and V during JARPA II for each survey mode estimated in this analysis. Detection functions toestimate ESHW for abundance estimation are shown in Figures 6a-6b. By pooling strata where thenumber of detections is less than 15, the shape of the detection functions seem improved, especially as
regards displaying a shoulder. Table 7 shows interpolated abundance estimate by the equation (20) for
strata which survey coverage was incomplete listed in Table 3. On implementation of models (7) – (10),abundance estimates in Table 6a-6j were replaced by those in Table 7 for such strata. Abundance
estimates for Areas IV and V for SSV, SVC and SVP survey modes during the JARPA period to apply
log-linear models (7) – (10) were referred from Hakamada et al. (in press).
Log-linear models and abundance trend estimates taking “model error” into accountTable 8 shows AICc and its difference from the selected model for each model, and the instantaneous
annual rates of increase and “model error” estimates for Areas IIIE+IV and V+VIW together with their
95% confidence intervals. Model i) was selected for both Areas IIIE+IV and V+VIW. These rates ofincrease (ROI) are 1.1% with a 95% CI of [-2.3%, 4.5%] for Area IIIE+IV and 0.6% with a 95% CI of[-2.2%, 3.3%] for Area V+VIW (Table 8). The point estimates from the other models range from 1.1% to4.4% for Area IIIE+IV and from -2.1% to 0.6% for Area V+VIW, so that all lie within the 95% CI for the10

SC/F14/J3

abundance trend estimate for the model selected (Table 8). Previous analyses of these data (Haw, 1991;
Branch and Butterworth 2001; Branch 2006; Hakamada et al. 2006) have suggested that a survey mode
calibration factor should be taken into account in developing composite abundance estimates, but that
option was not selected by AICc in this instance, possibly as a result of now allowing also for additionalvariance (model/process error) in the models of equations (7) to (10); including survey mode in theanalysis would not change the point estimate of trend in abundance for Area IIIE+IV, though that for Area
V+VIW would decrease by about 1% pa. Estimated coefficients for the log-linear model selected for each
Area are shown in Table 9. The model errors (ML estimates of the additional variance parameter σ) andtheir associated standard errors are 0.755 (SE=0.069) for Area IIIE+IV and 0.575 (SE=0.059) for Area
V+VIW. These estimates are negatively biased because they were obtained using a standard ML rather
than a restricted maximum likelihood (REML) approach (Patterson and Thompson, 1971).
Abundance estimates averaged over survey modes
Because model i) was selected for Areas IIIE+IV and V+VIW for the base case, adjustment factors were
not applied to abundance estimates in Tables 6a-6f.
Table 10 shows adjusted abundance estimates for each year and survey mode together with their CVs
when taking model error estimates into account. The inverse variance weighted averages of abundance
estimates over survey modes are shown in Table 11a. The CVs for these abundance estimates are all
higher than those from a previous analysis (Hakamada et al., 2006) because model error is now taken into
account.
Table 11b shows the inverse variance weighted averages of abundance estimates over survey modes
without taking model errors into account2.
Table 12 shows the correlation matrices for the logarithms of the abundance estimates given Areas IIIE,
IV, VW, VE and VIW, respectively. Correlations amongst these estimates are low because no common
correction factor was applied.
Adjustment for g(0) less than 1
The regression model of equation (21) was applied to school size information for the JARPA and JARPA
II survey strata, treating these in exactly the same way as for the OK estimates when developing that
regression equation. The resultant values are listed in Table 13. The abundance estimates in Tables 6a-6i
were then divided by these g(0) values to provide the g(0)-adjusted abundance estimates and their trends
which are shown in Tables 14 and 15. However, to avoid extrapolation when applying equation (21), in
the few cases where the regression estimate lay outside the range of the OK estimates of g(0) used to fit
the regression, those estimates were increased or decreased to equal to the lowest or highest value in theset of OK estimates. In computing variances estimates for the g(0)-adjusted abundance estimates andtrends given in Tables 14 and 15, appropriate account was taken of the co-variances introduced by the useof the common regression relationship of equation (21), though for simplicity the variances andco-variances for the OK estimates of g(0) were overlooked and these were treated as known without error
in estimating the regression parameter values.
Abundance estimates and trends for the sensitivity tests
Table 14 compares abundance estimates and their trends for the sensitivity tests examined. Different fromthe results in previous analysis (Hakamada et al., in press), models which allows for the effects of survey
mode and survey time together, was selected in all sensitivity test. For Areas IV, abundance estimates are
not always robust (-10.5% - 24.0% in average) among the sensitivities besides the g(0) adjusted scenario,
2

It should be noted here that the abundance estimates in Table 11b were recommended for their use in other studies
such as in the development of ecosystem models (Kitakado et al., 2014a); SCAA (Punt et al., 2013) and prey
consumption estimation (Tamura and Konishi, 2014), because those estimates had lower CVs than some initial
estimates that took into consideration the model error (the latter estimates had particularly high CVs -data notshown-). Improved recent analyses allowed that the CVs of the estimates that took into consideration model error
became much smaller (see Table 11a). Because of a timing problem, the studies on ecosystem model development,
SCAA and prey consumption were still using the estimates in Table 11b and could not be updated for the use of theestimates in Table 11a.

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because survey mode and survey time effect in the selected model affect on the abundance estimate.
model. For Area V, abundance estimates are not always robust (-7.7% - 18.8%) among the sensitivities
besides the g(0) adjusted scenario, because of the survey mode and survey timing effect of the selectedmodel. Table 15 shows estimated instantaneous annual rates of increase for Areas IIIE+IV and V+VIW
using the model selected by AICc for all the sensitivities examined in this paper. Using density instead ofabundance estimates for these calculations does not change the trend estimates substantially. These annual
abundance rate of increase estimates range over [0.1%, 3.7%] for Area IIIE+IV and [-1.7%, 0.6%] for
Area V+VIW for the various sensitivity tests.
When the abundance estimates are g(0)-adjusted, as would be expected the estimates increase by anaverage of 23,984 (88%) for Area IV and 105,906 (109%) for Area V (Table 14). The estimates of annual
rates of increase and their 95% CIs change to 2.5% [-1.3%,6.3%] for Areas IIIE and IV and -0.6 %[-3.9%,2.6%] for Areas V and VIW (Table 15), reflecting 1.5% increase for the former and 1% decrease
for the latter, and slightly less precision (a increase in standard error of about 0.02) than when g(0) is
assumed to be 1 because of the further variance introduced in estimating the g(0) values.

DISCUSSION AND FUTURE WORKThis paper has provided new estimates of abundance and trends for the Antarctic minke whales in Areas
IIIE, IV, V and VIW which take into account the recommendations made at the JRM. The CVs of theestimated abundances and trends were obtained with the incorporation of “model errors”. Information on
stock structure in Areas IIIE, IV, V and VIW has been provided based on both genetic and non-genetic
data from JARPA (Pastene et al., 2006). The JRM agreed that this showed there were at least two stocksof Antarctic minke whales present in the JARPA/JARPA II research area, and that the data suggests anarea of transition around 150 ºE -165ºE within which there is a yet undetermined level and range of
mixing (IWC, 2008b). Recently, the longitudinal extent of the transition area and the annual changes instock proportions in this region are being investigated using both genetic and non-genetic markers(Kitakado et al., 2014b). Because the distributions of these two stocks are not identical to theManagement Areas, it will be desirable to estimate abundance trends at the stock level, taking account
this recent information on the stock structure.
Clearly the estimates of abundance of this paper are subject to the same uncertainties as those from the
IDCR-SOWER surveys with regard to Antarctic minke whales in the unsurveyed areas south of the iceedge. At the IWC SC meeting in 2012, it was recognized that reliable absolute abundance estimates ofAntarctic minke whales in these ice regions (which are comparable in space and time for JARPA/JARPA
II and IDCR-SOWER surveys) would be impossible to produce. Accordingly, the recommendation wasmade that relatively simple analyses be conducted to generate abundances using aerial survey data (IWC,2013). Such abundance estimate using aerial survey data will be available in future. To the extent that it
might prove possible to use these to adjust the IDCR-SOWER abundance estimates, such an adjustment
process could also be applied to the abundance estimates based on JARPA and JARPAII.
Log-linear models
Because the extent of a same stratum varies from year to year as a result of different ice edge locations, it
is not immediately obvious whether such modelling approaches should be based on the density or on theabundance in a stratum, and arguments can be offered to support either approach. Matsuoka et al. (2011)
found little difference in results for the two approaches for humpback whales. This is also the case for the
Antarctic minke whale abundances and their trends as indicated in Table 15. For the selected model, theestimates of abundance trends would not change substantially if abundance were replaced by density inthe analyses.
Unlike the results of sensitivity analysis in Hakamada et al. (in press), models iii) or iv) were selected in
all cases of sensitivity test in this study, which may suggest that the abundance estimates are affected by
the survey mode and the survey timing effect. This may caused by an increased power to detect the effectdue to further survey data used. This may suggest that the effect of survey timing on abundance exists.
Even if the effect exists, abundance trend estimate varies within the 95% CI of abundance trend of theselected model for the base case for Areas IIIE+IV and V+VIW (Table 8).

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Under estimation of additional variance
In principle, additional variance (the size of model error) should be estimated using REML rather than
MLE to avoid negative bias, but this would lead to difficulties in model selection. This is a matter merits
future investigation.
Abundance trend estimates from JARPA
Figure 7 compares the exponential trend estimated by model i) with the estimates of abundance by year
for the base case model for each of Area IV and V. The exponential trend estimates are given in Table 8
and abundance estimates by year are given in Table 14.
For all the models and sensitivities examined, the point estimates of the abundance trend in Areas IIIE+IV
and V+VIW fall within the 95% confidence intervals for the model selected, even though different
log-linear model was selected in sensitivity test. This suggests the robustness of the abundance trend inboth Areas IIIE+IV and V+VIW.
Adjustment for g(0) < 1 and comparison with the minke whale abundance estimate derived from
IDCR-SOWER
Figure 8 repeats Figure 7 with the base case estimates of abundance replaced by g(0)-adjusted estimates.
Other than an approximate doubling of abundance in absolute terms, these Figures are very similar.
Figure 8 also shows the IWC-SOWER estimates for Areas IV and V from the second and third
circumpolar cruises as agreed by the 2012 IWC SC meeting (IWC, 2013). In three of the four cases there
is very good agreement between the IWC-SOWER point estimates and the exponential trend estimatedfrom applying model i) to the JARPA and JARPA II data. The exception is the point estimate for the
1985/86 CP II estimate for Area V which is appreciably larger than the following JARPA, JARPA II andCP III estimates. However, when the confidence intervals for both the CP II estimate and the exponential
trend are considered (see Figure 8), together with the fact that considerable backward extrapolation of thattrend is needed for comparison with the IDCR-SOWER estimate during a period when the actual (log-)
population trend might not have been linear, it is evident that there is no obvious inconsistency.
Nevertheless, comparison of Area V estimates on a finer spatial scale to identify the main source of thisdifference would seem desirable to aid understanding. Both the JARPA/JARPA II and IDCR-SOWER CIs
shown in Figure 8 incorporate additional variance (σ2 – see equations (7)-(10) and the text following).
The CIs for the JARPA and JARPA II surveys are notably larger, arising from a σ value of about 0.76
(Areas IIE+IV) and 0.58 (V+VIW), which are larger than that for the IDCR-SOWER surveys. Thisadditional variance arises from differing proportions of the overall population in a particular region
surveyed from one season to another; the reasons for the larger values for the JARPA and JARPA II
surveys merit further investigation, but may relate to the fact that these surveys extended over a longer
period than the IDCR-SOWER surveys (typically about three to about two months respectively), hence
allowing for more movement of minke whales into and out of the Areas while these were under survey.
Clearly, there is scope to attempt to improve the regression method used to provide g(0)-adjusted
abundance estimates in this paper by investigating the inclusion of other co-variates, such as those related
to sighting conditions such as Beaufort sea state. The regression approach may introduce bias, as the formof the detection function for the OK approach differs from that assumed for this analysis in equation (19);the size of this bias could be investigated by applying the methods of this paper to the data
IDCR-SOWER data and comparing the results to those obtained using the OK method of Okamura andKitakado (2012). Furthermore, in JARPA II the SVs use Closing mode and Passing mode where the latter
now includes an Independent Observer (Matsuoka et al., 2012). The sighting survey was canceled due toby anti-whaling NGOs but it could be conducted in future surveys. The availability of data on duplicate
sightings will allow the application of methods such as OK and SPLINTR to estimate g(0) directly, which
will hopefully reduce variance compared to the g(0)-adjusted estimates of this paper and hence improve
estimates of trends in abundance for the minke whales based on JARPA and JARPA II information in
combination..
Application of JARPA and JARPA II abundance trend
One of the features of JARPA and JARPA II is that, unlike for the IDCR-SOWER programme, surveys
have been repeated in the same area and in the same months every second year over a long period.

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Therefore, the JARPA and JARPA II surveys facilitate both estimation of trends and the extent ofinter-year variability in local abundance. These abundance series as well as those from IDCR/SOWER
can be used to estimate abundance trends using population dynamics models which incorporate
catch-at-age data and so integrate information from a number of different sources (Punt et al., 2013; Moriet al., 2006). Through their use in such population models, the abundance estimates and trends derived
from JARPA and JARPA II which are reported in this paper provide information to complement thatavailable to estimate sustainable catch levels for minke whales in Areas IIIE, IV, V and VIW.

ACKNOWLEGEMENTS
We would like to express our gratitude for Doug Butterworth and Hiroshi Hatanaka for their valuablecomments to improve the manuscript. We would like to express our gratitude for Hiroshi Okamura forkindly providing g(0) estimates from and school size data used for the OK model, as well as for his usefulsuggestions to improve the manuscript. In addition we thank Luis. Pastene for improving an earlier
version of this paper, and Nobuyuki Suzuki and Satoshi Suzuki for their assistance in dealing withsighting data for the analyses and producing the figures for this paper. Naturally this work would not havebeen possible without the considerable and appreciated efforts of all the researchers, Captains, officers
and crew members who have participated in the JARPA and JARPA II surveys.

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Okamura, H and Kitakado, T. 2012. Abundance estimates of Antarctic minke whales using the OK
method. Paper SC/64/IA2 submitted to IWC Scientific Committee, June 2012. (unpublished). 24pp.
[Available from the Office of this Journal].
Pastene, A, L. 2006. What do we know about the stock structure of the Antarctic minke whale? Asummary of studies and hypotheses. Paper SC/D06/J12 presented to the JARPA review meeting.
(unpublished). 24pp. [Available from the Office of this Journal].
Patterson, H. D. and Thompson, R. 1971. Recovery of interblock information when block size areunequal. Biometrika 58: 545-54.
Punt, A. E., Bando,T. Hakamada, T. and Kishiro, T. 2013. Assessment of Antarctic Minke Whales using
Statistical Catch-at-age Analysis. Paper SC/65a/IA01 submitted to the IWC Scientific Committee, June
2013. (unpublished). 48pp.
Sugiura, N. 1978. Further analysis of the data by Akaike's information criterion and the finite corrections.
Communication in Statistics A(7): 13-26.
Tamura, T. and Konishi, K. 2014. Prey composition and consumption rate by Antarctic minke whales
based on JARPA and JARPAII data. Paper SC/F14/J15 submitted to JARPA II review meeting.
(unpuplished). 20pp.
Thomas, L., S.T. Buckland, E.A. Rexstad, J.L. Laake, S. Strindberg, S.L. Hedley, J.R.B. Bishop, T.A.
Marques, and K.P. Burnham. 2010. Distance software: design and analysis of distance sampling
surveys for estimating population size. Journal of Applied Ecology 47: 5-14.

16

SC/F14/J3

Table 1. Smearing parameters for each survey mode in JARPA II period used in abundance estimation.
Units for angles are degrees which for radial distance the values given are proportions.
Year
2005/06
2006/07
2007/08
2008/09

SVC
Angle
Distance
11.003
0.199
7.000
0.220
4.848
0.136
5.780
0.157

SVP
Angle
Distance
6.038
0.177
4.975
0.171
6.099
0.202
3.782
0.144

Table 2. List of the factors which the sensitivity of abundance estimates and/or trends is examined.
Specifications are given for both the base case and the sensitivities, with more details provided in
Hakamada et al. (in press).
Sensitivity factors

Specifications for the base caseSpecifications for sensitivities‘Shoulder’ of detection function

Estimation by stratum, except thatwhen sample size is less than 15, strata
are pooled.
Complete tracklines used.

For SSVs, ESHW averaged over the vessels concerned wasused. For SVs the detection function estimation takes
account of covariates.
(1)Exclude trackline segments along the ice edge (Option
B). (2)Use only the transects parallel to lines of longitude(Option C).
(1)Assume the density is 0. (2)Assume the same density asin the stratum to the south.
Extrapolate based on average ratio of density in theunsurveyed to surveyed area as estimated in other years withcomplete coverage for JARPA.

Trackline following ice edge
contours
Abundance in gaps between
northern and southern strata
Interpolation of density in theunsurveyed area within astratum“Skipping”

Assume same density as in stratum tothe north.
Estimated density assumed to apply tocompletestratumfor
JARPA.
Interpolation using GLM was used for
JARPA II.
Assumed not to introduce bias.

g(0)Assumed to equal 1

Exclude the abundance estimates for years when “skipping”
occurred when.estimating trends.
Adjust for g(0) estimates provided by the regression model
detailed in the text.

Table 3. List of strata which survey coverage was considered incomplete during 2005/06-2008/09.
Year
Area IIIE

StrataSurvey mode Longitudinal sector

2005/06

IIIEN

SVC,SVP

35 oE-55oE

2005/06
IIIES
Area IV
2007/08 Prydz Bay
Area V

SVC,SVP

35 oE-55oE

SVC,SVP

Whole strata

2005/06
2005/06

V-NW
V-SW

SVC,SVP
SVC,SVP

159 oE-165 oE
159 oE-165 oE

2006/07

V-NW

SVC,SVP

130 oE-159 oE

2006/07
2007/08

V-SW
V-NW

SVC,SVP
SVC,SVP

130 oE-159 oE
161 oE-165 oE

2007/08

V-SW

SVC

161 oE-165 oE

2008/09

V-NW

SVC,SVP

161 oE-165 oE

17

SC/F14/J3

Table 4. Status of survey in each year for blocks to apply equation (20). Definition of the blocks areprovided in main text. S: surveyed U: Not surveyed.
Area IIIE
Year
2005/06
2007/08

IIIEN1 IIIEN2 IIIES1 IIIES2
U
S
U
S
S
S
S
S

Area IVW
Year
2005/06
2007/08
Area VW
Year
2005/06
2006/07
2007/08
2008/09

PB
S
U

SW
S
S

NW
S
S

5WN1 5WN2 5WS1 5WS2
S
U
S
U
U
S
U
S
S
U
S
S*
S
U
S
U

*: The block was surveyed by only in passing mode.

Table 5. Values of the parameters of the regression model of equation (21) relating OK estimates of g(0)to school size distribution statistics and other covariates for IDCR-SOWER survey blocks in Areas III, IV,
V and VI. The residuals standard derivation for the model fit is 0.08.
abcNS
AreaIV
AreaV
AreaVI
NS*AreaIV
NS*AreaIV
NS*AreaVI

Estimate
0.622
0.042
-0.064-0.066-0.019-0.039-0.032-0.028
0.067
0.104

18

SE
0.051
0.018
0.020
0.077
0.055
0.050
0.061
0.107
0.089
0.104

SC/F14/J3

Table 6a. The abundance estimates from SSV survey mode for minke whales in Area IIIE (south of 60oS)
in the JARPA period. A=size of research area; n=number of schools sighted on primary effort (truncatedat a perpendicular distance of 1.5 n.miles after smearing); L=primary searching distance; esw=the
effective search half width (hazard rate model estimate); E(s)=mean school size; D= estimated density(individuals/100 n.miles2); P=estimated abundance.
Year

StratumA

PeriodnL2(n.miles )1995/96
1997/98
1999/00
2001/02
2003/04

IIIE
IIIE
IIIE
IIIE
IIIE

n /L *100

CV(n.miles)

253,343
12/8-22
250,985 12/16-31
357,358 12/15-26
355,282
12/9-25
324,032 11/30-12/23

esw

CV

E (s )

CV

0.189
0.239
0.155
0.162
0.148

1.298
1.649
1.679
2.770
2.083

0.054
0.072
0.070
0.136
0.034

(n.miles)69.2 2431.2
76.7 3238.1
95.4 1256.0
123.3 2202.8
146.4 3377.7

0.028
0.024
0.076
0.056
0.043

0.190
0.320
0.485
0.552
0.352

0.444
0.761
0.765
0.696
0.835

D
(ind.)
0.042
0.026
0.083
0.111
5.407

P
(ind.)
10,546
6,435
29,79439,56017,522

CV
0.222
0.352
0.495
0.566
0.364

Table 6b. The abundance estimates from SSV survey mode for minke whales in Area VIW (south of 60oS)
in the JARPA period. The notation is as for Table 6a.
Year

StratumA

PeriodnL2(n.miles )1996/97
1998/99
2000/01
2002/03
2004/05

VIW
VIW
VIW
VIW
VIW

n /L *100 CV

esw(n.miles)

205,180 12/15-1/4 76.8 2625.5
316,727 3/16-31 69.6 572.5
290,908 12/11-31 112.8 2897.5
329,256 12/2-1/1 118.2 4398.3
292,218 12/7-25 117.9 2826.5

CV

DP(ind.) (ind.)
0.162 1.785 0.078 0.035 7,201
0.149 1.235 0.051 0.095 30,194
0.161 1.422 0.058 0.042 12,317
0.139 1.209 0.038 0.032 10,486
0.154 1.361 0.049 0.051 14,805(n.miles)

0.029
0.122
0.039
0.027
0.042

0.193
0.314
0.210
0.203
0.285

0.744
0.788
0.654
0.510
0.561

E (s )

CV

CV
0.220
0.327
0.232
0.219
0.300

Table 6c. The abundance estimates from SVC survey mode for minke whales in Area IIIE (south of 60oS)
in the JARPA and JARPA II period. The notation is as for Table 6a.
Year

StratumA

Periodn

2

1995/96 IIIE
1997/98 IIIE
1999/00 IIIE
2001/02 IIIE
2003/04 IIIE
2005/06 IIIEN
IIIES
Total
2007/08 IIIEN
IIIES
Total

L

n /L *100(n.miles )(n.miles)

253,343
12/8-225.0
250,985 12/16-30
6.0
356,046 12/5-13,21-26 4.0
354,965
12/9-2420.0
324,032 11/30-12/24 31.1
128,829
1/7-1910.8
30,323
1/7-1910.0
159,152
20.8
228,382 12/15-1/7
6.0
50,431 12/15-1/7
22.0
278,813
28.0

733.9
697.0
188.1
257.9
373.2
149.8
238.7
388.4
322.2
241.7
564.0

CV

esw

CV

E (s )CV

DP
CV(ind.) (ind.)0.486 0.178 0.561 1.000 0.000 0.019 4,843 0.742
0.440 0.372 0.984 1.000 0.000 0.012 2,902 1.078
1.296 0.428 0.422 1.667 0.400 0.041 14,749 1.420
0.548 0.384 0.265 3.889 0.249 0.392 139,277 0.658
0.5987 0.35491 0.5725 1.8071 0.1112 0.212 68,853 0.836
0.558 0.664 0.360 1.729 0.143 0.094 12,098 0.679
0.497 0.664 0.360 1.729 0.143 0.055 1,654 0.630
13,752 0.628
0.521 0.447 0.226 1.800 0.122 0.037 8,562 0.581
0.515 0.447 0.226 1.800 0.122 0.183 9,241 0.575
17,804 0.447(n.miles)

0.681
0.861
2.127
7.755
8.346
7.212
4.190
1.862
9.101

Table 6d. The abundance estimates from SVC survey mode for minke whales in Area IV (south of 60oS)
in the JARPA II period. The notation is as for Table 6a.
Year
2005/06

2007/08

Stratum
NW
NE
SW
SE
PB
Total
NW
NE
SW
SE
Total

A

PeriodnLn /L *100 CV

esw(n.miles2 )(n.miles)

228,919 1/3-6,2/1-15
8.5
213,660 2/17-24,3/5-12 6.0
47,117 1/3-6,2/1-15 31.9
37,228 2/17-24,3/5-12 2.8
31,689
1/20-1/31
22.6
558,61371.8
213,311 1/7-13,2/25-3/2 6.0
216,236
3/2-201.0
39,787 1/7-13,2/25-3/2 7.0
36,277
3/2-206.0
505,61120.0

297.5
2.856 0.417 0.609
462.7
1.297 0.424 0.609
227.8 13.987 0.841 0.407
189.6
1.478 0.677 0.407
95.1
23.775 0.368 0.483
1,272.6281.7
2.130 0.456 0.447
503.6
0.199 0.856 0.447
193.4
3.619 0.640 0.447
261.8
2.292 0.484 0.447
1,240.6(n.miles)19

CV

E (s )CV

DP
CV(ind.) (ind.)0.281 1.733 0.143 0.041 9,298 0.523
0.281 1.733 0.143 0.018 3,940 0.528
0.440 3.961 0.285 0.680 32,040 0.991
0.440 3.961 0.285 0.072 2,674 0.855
0.573 2.318 0.171 0.571 18,083 0.702
66,035 0.537
0.226 1.800 0.122 0.043 9,148 0.523
0.226 1.800 0.122 0.004 865 0.894
0.226 1.800 0.122 0.073 2,899 0.689
0.226 1.800 0.122 0.046 1,674 0.548
14,585 0.412

SC/F14/J3

Table 6e. The abundance estimates from SVC survey mode for minke whales in Area V (south of 60oS) in
the JARPA II period. The notation is as for Table 6a.
Year

Stratum

2005/06

NW
SW
2006/07 NW
NE
SW
SE
Total
2007/08 NW
SW
2008/09 NW
NE
SW
SE
Total

A

PeriodnLn /L *100 CV(n.miles2 )(n.miles)

238,068 12/9-22,3/11-20 8.0
49,999 12/9-22,3/11-20 22.5
38,740
2/12-151.0
340,889
1/2-1021.8
9,260
2/12-155.0
139,575
1/13-3169.8
528,46397.5
275,376
1/26-2/18
10.0
43,609
1/26-2/18
39.3
224,275
12/10-1/1
2.0
324,889 2/2-8,2/28-3/10 5.0
64,901
12/10-1/1
8.0
277,209
2/4-27133.3
891,274148.3

202.45156.49
22.9
560.3
12.4
607.9
1203.4
280.9
149.9
391.3
358.7
145.9
686.5
1582.3

esw

CV

3.952
14.3634.368
3.886
40.463
11.479

1.018
1.000
0.312
0.315
0.447
0.271

0.766
0.766
0.844
0.844
0.689
0.689

0.171
0.171
0.161
0.161
0.276
0.276

D
(ind.)
3.387 0.235 0.087
3.3871 0.235 0.317
2.722 0.190 0.070
2.722 0.190 0.063
1.421 0.071 0.418
1.421 0.071 0.118

3.561
26.201
0.511
1.394
5.484
19.420

0.923
0.543
0.666
0.558
0.849
0.161

0.865
0.865
0.591
0.591
0.591
0.591

0.311
0.311
0.196
0.196
0.196
0.196

3.564
3.564
2.173
2.173
2.173
2.173

(n.miles)E (s )

CV

P(ind.)
20,794
15,873
2,730
21,373
3,866
16,53144,5000.171 0.073 20,208
0.171 0.540 23,549
0.075 0.009 2,108
0.075 0.026 8,329
0.075 0.101 6,546
0.075 0.357 99,003
115,986

CV
1.059
1.041
0.399
0.402
0.530
0.393
0.265
0.988
0.649
0.698
0.596
0.875
0.264
0.259

Table 6f. The abundance estimates from SVC survey mode for minke whales in Area VIW (south of 60oS)
in the JARPA and JARPA II period. The notation is as for Table 6a.
Year

StratumA

Periodn2(n.miles )1996/97 VIW
2000/01 VIW
2002/03 VIW
2004/05 VIW
2006/07 VIWN
VIWS
Total
2008/09 VIWN
VIWS
Total

L

n /L *100 CV

esw(n.miles)

206,490 12/14-1/1
290,908 12/11-29
309,998 12/3-30
292,218 12/7-12/24
220,818 12/16-30
31,008 12/16-30
251,826
275,376 1/10-31
43,609 1/1-2/2
318,98529.6 1015.7
30.0 660.6
8.0 354.7
7.0 243.0
5.0 194.4
9.0 189.6
14.0 384.0
2.0 263.5
34.8 235.1
36.8 498.5

CV

E (s )

CV

D(ind.)
0.410 0.442 2.044 0.193 0.073
0.835 0.189 2.071 0.143 0.056
0.466 0.286 1.500 0.333 0.036
0.388 0.297 1.286 0.143 0.048
0.844 0.161 2.722 0.190 0.041
0.844 0.161 2.722 0.190 0.077

P(ind.)
14,994
16,403
11,26013,9489,164
2,376
11,5390.432 0.389 1.277 0.092 0.011 1,870
0.432 0.389 1.277 0.092 0.219 16,677
18,547

CV(n.miles)2.913 0.236
4.547 0.280
2.255 0.523
2.881 0.378
2.572 0.351
4.748 1.368
0.759 1.309
14.794 0.309

0.537
0.367
0.683
0.502
0.430
1.390
0.468
1.369
0.505
0.504

Table 6g. The abundance estimates from SVP survey mode for minke whales in Area IIIE (south of 60oS)
in the JARPA and JARPA II period. The notation is as for Table 6a.
Year

StratumA(n.miles2 )1997/98 IIIE
1999/00 IIIE
2001/02 IIIE
2003/04 IIIE
2005/06 IIIEN
IIIES
Total
2007/08 IIIEN
IIIES
Total

PeriodnLn /L *100

CV(n.miles)

250,985 12/16-30
2.0 309.1
356,046 12/5-13,21-26 5.0 527.8
354,965
12/9-24
19.1 426.2
324,032 11/30-12/24 154.5 1490.4
128,829
1/7-1965.6 524.4
30,323
1/7-1946.3 436.3
159,152111.9 960.8
228,382 12/15-1/7
5.0 609.2
50,431 12/15-1/7
29.6 884.4
278,81334.6 1493.6

esw(n.miles)

0.647
0.908
0.947
0.610
4.487
0.465
10.363 0.4024
12.507 0.163
10.616 0.239

1.500
0.361
0.680
0.756
0.702
0.513

0.821
3.344

0.379
0.379

0.448
0.271

20

CV

E (s )

CV

D
P(ind.) (ind.)0.000 1.000 0.000 0.002
541
1.415 1.667 0.400 0.022 7,782
0.385 3.889 0.249 0.128 45,569
0.1815 1.8071 0.1112 12.384 40,128
0.158 1.729 0.143 0.154 19,834
0.364 1.729 0.143 0.179 5,424
25,2580.447 1.800 0.122 0.019 4,448
0.447 1.800 0.122 0.079 4,002
8,450

CV
0.908
1.592
0.653
0.455
0.268
0.458
0.247
0.645
0.537
0.535

SC/F14/J3

Table 6h. The abundance estimates from SVP survey mode for minke whales in Area IV (south of 60oS)
in the JARPA II period. The notation is as for Table 6a.
Year

StratumA

Periodn2(n.miles )2005/06 NW
NE
SW
SE
PB
Total
2007/08 NW
NE
SW
SE
Total

L

n /L *100CVesw(n.miles)

228,919 1/3-6,2/1-15
213,660 2/17-24,3/5-12
47,117 1/3-6,2/1-15
37,228 2/17-24,3/5-12
31,689 1/20-1/31
558,613
213,311 1/7-13,2/25-3/2
216,236 3/2-20
39,787 1/7-13,2/25-3/2
36,277 3/2-20
505,611CV

E (s )CV

DP
CV(ind.) (ind.)0.096 0.071 16,149 0.322
0.096 0.012 2,587 0.379
0.096 0.411 19,387 0.360
0.096 0.110 4,101 0.496
0.096 0.097 3,081 0.400
45,305 0.225
0.100 0.050 10,668 0.319
0.100 0.002
520 0.852
0.100 0.058 2,298 0.521
0.100 0.023
845 0.638
14,330 0.275(n.miles)

32.5
833.9
6.6
987.6
143.7
631.7
41.2
675.9
15.4
285.9
239.4 3,415.0
17.0 677.2
1.0 828.8
19.0 654.1
6.5 558.0
43.5 2,718.1

3.901
0.669
22.751
6.091
5.376

0.298
0.359
0.339
0.481
0.380

0.660
0.660
0.660
0.660
0.660

0.077
0.077
0.077
0.077
0.077

2.388
2.388
2.388
2.388
2.388

2.510
0.121
2.900
1.169

0.279
0.838
0.498
0.619

0.553
0.553
0.553
0.553

0.117
0.117
0.117
0.117

2.204
2.204
2.204
2.204

Table 6i. The abundance estimates from SVP survey mode for minke whales in Area V (south of 60oS) in
the JARPA II period. The notation is as for Table 6a.
Year

StratumA

PeriodnL2(n.miles )2005/06
2006/07

2007/08
2008/09

NW
SW
NW
NE
SW
SE
Total
NW
SW
NW
NE
SW
SE
Total

n /L *100

CV17.88814.04214.7940.969
11.13215.8250.524
0.662
0.516 0.88225
0.255
0.780
0.241
0.780
0.490
0.812
0.153
0.812

0.169
0.1514
0.363
0.363
0.102
0.102

3.387
3.3871
2.722
2.722
1.421
1.421

0.235
0.235
0.190
0.190
0.071
0.071

D
(ind.)
0.457
0.270
0.258
0.017
0.097
0.139

3.239
11.185
0.133
1.385
3.381
28.074

0.318
0.299
0.952
0.500
0.383
0.196

0.507
0.136
0.483
0.483
0.625
0.077

3.564
3.564
2.173
2.173
2.173
2.173

0.171
0.171
0.075
0.075
0.075
0.075

0.116
0.348
0.005
0.053
0.065
0.268

(n.miles)

238,068 12/9-22,3/11-20
49,999 12/9-22,3/11-20
38,740 2/12-15
340,889 1/2-10
9,260 2/12-15
139,575 1/13-31
528,463
275,376 1/26-2/18
43,609 1/26-2/18
224,275 12/10-1/1
324,889 2/2-8,2/28-3/10
64,901 12/10-1/1
277,209 2/4-27
891,274

73.6 411.32
69.7 496.156
11.0
74.4
15.0 1547.5
13.8
123.9
263.5 1665.0
303.3 3410.7
28.1
867.3
80.0
715.0
1.0
753.4
14.0 1010.9
16.6
492.3
581.5 2071.3
613.1 4327.8

esw

CV

E (s )

CV(n.miles)

0.499
0.573
0.286
0.286
0.569
1.138

P
(ind.)
108,895
13,477
10,001
5,766
903
19,34336,01331,84815,165
1,132
17,114
4,190
74,271
96,707

CV
0.598
0.587
0.482
0.475
0.505
0.197
0.370
0.622
0.370
1.071
0.699
0.737
0.223
0.221

Table 6j. The abundance estimates from SVP survey mode for minke whales in Area VIW (south of 60oS)
in the JARPA and JARPA II period. The notation is as for Table 6a.
Year

StratumA

Period2(n.miles )1996/97 VIW
2000/01 VIW
2002/03 VIW
2004/05 VIW
2006/07 VIWN
VIWS
Total
2008/09 VIWN
VIWS
Total

n

L

n /L *100 CV(n.miles)

206,490 12/14-1/1 29.6 1015.7
290,908 12/11-29
30.0 660.6
309,998 12/3-30
8.0 354.7
292,218 12/7-12/24 7.0 243.0
220,818 12/16-30
7.8 561.95
31,008 12/16-30
19.0 531.68
251,82626.8 1093.6
275,376 1/10-31
1.0 458.09
43,609 1/1-2/2
183.0 755.04
318,985184.0 1213.1

esw

CV

E (s )

CV

D(ind.)
0.410 0.442 2.044 0.193 0.073
0.835 0.189 2.071 0.143 0.056
0.466 0.286 1.500 0.333 0.036
0.388 0.297 1.286 0.143 0.048
0.439 0.296 2.722 0.190 0.043
0.439 0.296 2.722 0.190 0.111

P(ind.)
14,994
16,403
11,26013,9489,469
3,435
12,9040.490 0.140 1.277 0.092 0.003474
0.490 0.140 1.277 0.092 0.316 24,109
24,584CV(n.miles)2.913 0.236
4.547 0.280
2.255 0.523
2.881 0.378
1.384 0.973
3.574 0.634
0.218 0.763
24.237 0.211

21

0.537
0.367
0.683
0.502
1.034
0.725
0.813
0.781
0.269
0.266

SC/F14/J3

Table 7. Interpolated abundance estimate using model (20) for strata which survey coverage considered
incomplete listed in Table 3.
Year
StrataArea IIIE
2005/06
IIIEN
2005/06
IIIES
Area IV
2007/08 Prydz Bay
Area V
2005/06
V-NW
2005/06
V-SW
2006/07
V-NW
2006/07
V-SW
2007/08
V-NW
2007/08
V-SW
2008/09
V-NW

P SVC

CV(PSVC)P SVP

CV(PSVP)

20,444
3,817

0.597
0.657

29,408
7,912

0.402
0.457

3,310

1.282

3,310

1.282

21,005
15,267
21,528
8,894
21,177
21,273
4,708

0.999
0.945
0.608
0.450
0.855
0.603
0.703

105,110
13,80734,2907,400
31,709
4,686

0.590
0.530
0.507
0.578
0.557
1.005

Table 8. AICc, estimated instantaneous annual rates of increase (α) and estimated additional variance (σ)
the various log-linear models applied to estimate α for Areas IIIE+ IV and Area V+VIW where the minke
whale abundance estimates input to those models are for the base case (i.e. the estimates shown in Tables
6a-6j and 7). Values shown in bold below are for the model selected on the basis on minimum AICc.
a) Areas IIIE + IV
model
i)
ii)
iii) with T1iii) with T2iii) with T3iii) with T4iv) with T1iv) with T2iv) with T3iv) with T4

AIC_c135.274
139.435
142.479140.406
136.768
141.357
145.970
139.499135.967
141.638α∆ AIC_c
0.00
4.16
7.21
5.13
1.49
6.08
10.70
4.23
0.69
6.36

0.011
0.016
0.023
0.023
0.026
0.018
0.040
0.044
0.037
0.026

∆AIC c
0.00
1.17
3.48
2.35
2.20
0.63
12.56
8.79
1.63
0.24

0.006
-0.008-0.012-0.010-0.011-0.014-0.010-0.005-0.010-0.021SE (α )
0.017
0.019
0.020
0.020
0.019
0.020
0.021
0.021
0.019
0.020

α 95%LL

α 95%UL

σ-0.023-0.022-0.016-0.016-0.011-0.021-0.0020.003
0.000-0.014

0.045
0.054
0.061
0.062
0.064
0.057
0.082
0.086
0.075
0.065

0.755
0.754
0.740
0.744
0.702
0.756
0.628
0.677
0.636
0.718

SE (α )
0.014
0.016
0.015
0.015
0.015
0.015
0.016
0.015
0.015
0.016

α 95% LL

α 95%UL

σ-0.022-0.039-0.042-0.040-0.040-0.044-0.042-0.035-0.040-0.053

0.033
0.023
0.018
0.020
0.018
0.016
0.022
0.025
0.019
0.010

0.575
0.560
0.531
0.540
0.499
0.525
0.454
0.485
0.407
0.474

b) Areas V + VIW
Model
i)
ii)
iii) with T1iii) with T2iii) with T3iii) with T4iv) with T1iv) with T2iv) with T3iv) with T4

AICc
68.80269.97172.27971.15471.00669.43281.35977.59670.43669.041α22SE (σ )
0.069
0.069
0.068
0.068
0.065
0.069
0.063
0.065
0.062
0.067

SE (σ )
0.059
0.059
0.058
0.059
0.056
0.058
0.058
0.058
0.055
0.057

SC/F14/J3

Table 9. Estimated coefficients of the log-linear models selected on the basis of AICc to provide estimatesof the rate of increase in minke whale abundance, α.
a)Areas IIIE+IV
Parameter
factor(S)SW
factor(S)SE
factor(S)NW
factor(S)NE
factor(S)PB
factor(S)IIIEασb)Areas V+VIW
Estimate
8.318
8.143
8.772
8.575
8.447
9.609
0.011
0.755

SE
0.252
0.252
0.262
0.265
0.265
0.314
0.017
0.069

Parameter
factor(S)SW
factor(S)SE
factor(S)NW
factor(S)NE
factor(S)VIW

α

σ

23

Estimate
9.336
10.373
9.858
9.917
9.591
0.006
0.575

SE
0.220
0.222
0.222
0.225
0.257
0.014
0.059

SC/F14/J3

Table 10. Abundance estimates for Areas IIIE, IV, VW, VE and VIW based upon data for each survey
mode separately.
Area IVYear
1989/90
1991/92
1993/94
1995/96
1997/98
1999/00
2001/02
2003/04
2005/06
2007/08

PSSV CV(PSSV)
29,9930.527
32,4180.720
27,9890.539
28,9190.579
17,2720.763
42,8520.495
46,3550.660
59,918
1.031-

P SVC CV(PSVC ) PSVP CV(PSVP)
26,5460.909
51,2640.703
15,9360.776
74,3120.739
42,8580.555
24,471
0.579
58,1760.744
25,9100.969
48,9841.050
66,035 0.815
62,608 0.617
17,896 0.740
13,766 0.759

Area VW

Area VE

Year
1990/91
1992/93
1994/95
1996/97
1998/99
2000/01
2002/03
2004/05
2005/06
2006/07
2007/08
2008/09Year
1990/91
1992/93
1994/95
1996/97
1998/99
2000/01
2002/03
2004/05
2006/07
2008/09

PSSV CV(PSSV) P SVC CV(PSVC ) PSVP CV(PSVP)
35,1080.575
22,4040.625
11,7640.604
22,3860.727
14,4340.574
26,7080.800
55,5770.746 110,993 1.315
14,6261.053
48,616
1.632
54,2851.016
53,6820.562
72,1390.869 131,686 0.545
27,6960.629
20,6790.708
31,4370.644
36,272 0.944 118,917 0.875
30,422 0.765
41,6900.754
42,4500.753
46,5880.722
11,586
0.708
9,547
0.761

PSSV CV(PSSV) P SVC CV(PSVC ) PSVP CV(PSVP)
65,6380.597
43,7430.631
73,4950.676 130,091 0.659
74,9810.701
91,8910.681
62,4400.602
21,6730.816
87,2280.492 284,474 1.765 131,735 0.980
97,6770.720
39,9630.605
73,6660.611
40,7290.519
59,2220.668
53,4920.622
37,904 0.553
25,109 0.536
107,332 0.640
91,385 0.542

Area IIIE

Area VIW

Year
1995/96
1997/98
1999/00
2001/02
2003/04
2005/06
2007/08Year
1996/97
1998/99
2000/01
2002/03
2004/05
2006/07
2008/09

PSSV CV(PSSV) P SVC CV(PSVC ) PSVP CV(PSVP)
10,546
1.086
4,843
1.285
6,435
0.967
2,902
1.659
29,7941.016
14,749
1.328
7,782
1.472
39,5601.059 139,277 1.129
45,5691.465
17,5220.979
68,8531.554
40,1281.342
24,261 1.090
37,320 1.156
17,804 0.907
8,450
1.209

24

PSSV CV(PSSV)
7,201
0.697
30,1940.707
12,3170.682
10,4860.742
14,8050.735-

P SVC CV(PSVC ) PSVP CV(PSVP)
14,9940.953
16,4030.785
11,2601.185
29,4940.837
13,948
0.845
8,792
0.899
11,5390.826
17,9260.651
18,547 0.751
12,904 1.072
34,941 0.812
24,584 0.617

SC/F14/J3

Table 11a. Weights (W) (see text) and the weighted average over survey modes to provide a minke whale
abundance estimate (PWA) for each year with taking model error into account.
Area IV
Year WSSV WSVC WSV P P WA CV(PWA)
1989/90 1.00 29,993 0.527
1991/92 1.00 32,418 0.720
1993/94 0.73 0.27
27,598 0.473
1995/96 0.82 0.18
32,970 0.458
1997/98 0.47 0.53
16,562 0.542
1999/00 0.53 0.07 0.41 44,945 0.338
2001/02 0.16 0.76 0.08 30,807 0.402
2003/04 0.11 0.74 0.15 32,970 0.682
2005/06 0.35 0.65 63,794 0.509
2007/08 0.32 0.68 15,088 0.645

Area VW
Year WSSV WSVC WSV P
1990/91 1.00 1992/93 1.00 1994/95 0.87 0.13
1996/97 0.83 0.17
1998/99 0.97 0.03
2000/01 0.80 0.05 0.15
2002/03 0.59 0.24 0.17
2004/05 0.32 0.48 0.20
2005/06 0.87 0.13
2006/07 0.71 0.29
2007/08 0.45 0.55
2008/09 0.24 0.76

Area VEPWA CV(PWA)
35,1080.575
22,4040.625
13,132
0.482
16,5350.480
57,4780.668
22,115
1.105
71,2100.382
25,1080.390
47,0350.769
33,6550.638
44,7360.549
10,031
0.569

Year WSSV WSVC WSV P
1990/91 1.00 1992/93 1.00 1994/95 0.74 0.26
1996/97 0.58 0.42
1998/99 0.39 0.61
2000/01 0.93 0.01 0.06
2002/03 0.06 0.64 0.31
2004/05 0.58 0.15 0.27
2006/07 0.29 0.71
2008/09 0.47 0.53

Area IIIE

Area VIW

Year WSSV WSVC WSV P P WA CV(PWA)
1995/96 0.36 0.64
6,897
0.914
1997/98 0.45 0.55
4,478
0.911
1999/00 0.13 0.06 0.80 11,205 0.862
2001/02 0.87 0.09 0.04 48,540 0.711
2003/04 0.86 0.01 0.13 21,133 0.782
2005/06 0.65 0.35 28,822 0.807
2007/08 0.19 0.81 10,237 0.778

Year WSSV WSVC WSV P
1996/97 0.84 0.16
1998/99 0.23 0.77
2000/01 0.58 0.36 0.06
2002/03 0.47 0.09 0.45
2004/05 0.30 0.47 0.23
2006/07 0.49 0.51
2008/09 0.31 0.69

25

PWA CV(PWA)
65,6380.597
43,7430.631
88,311
0.489
82,0650.498
37,4660.558
91,1970.445
53,6030.398
46,9410.353
28,8180.400
98,8200.422

PWA CV(PWA)
8,434
0.601
19,5920.537
12,891
0.507
10,0300.480
14,0090.430
15,6550.625
27,7900.507

SC/F14/J3

Table 11b. Weights (W) (see text) and the weighted average over survey modes to provide a minke whale
abundance estimate (PWA) for each year without taking model error into account.
Area IV
Year WSSV WSVC WSV P P WA CV(PWA)
1989/90 1.00 29,993 0.228
1991/92 1.00 32,418 0.396
1993/94 0.85 0.15
27,780 0.147
1995/96 0.88 0.12
31,601 0.198
1997/98 0.49 0.51
16,590 0.277
1999/00 0.59 0.03 0.38 43,673 0.125
2001/02 0.17 0.77 0.06 30,269 0.218
2003/04 0.22 0.72 0.05 34,701 0.373
2005/06 0.11 0.89 62,979 0.334
2007/08 0.24 0.76 14,739 0.570
Area VW

Area VEWSSV WSVC WSVP

PWA

CV(PWA)

1990/91
1992/93
1994/95
1996/97

1.00
1.00
0.90
0.91

0.10
0.09

-

35,10822,404
12,805
15,540

0.220
0.350
0.275
0.293

1998/99
2000/01
2002/03
2004/05

0.98
0.87
0.71
0.44

0.02
0.01
0.16
0.21

0.11
0.13
0.35

56,927
19,60366,54427,554

0.538
1.031
0.234
0.243

2005/06
2006/07
2007/08
2008/09-

0.89 0.11
1.00 0.00
0.35 0.65
0.484 0.516

45,54130,42245,157
10,534

0.701
0.534
0.408
0.638

Year

Area IIIE
Year
1995/96
1997/98
1999/00
2001/02
2003/04
2005/06
2007/08

Year

WSSV WSVC WSVP

1990/91 1.00
1992/93 1.00
1994/95 0.52
1996/97 0.58
1998/99 0.52
2000/01 0.98
2002/03 0.09
2004/05 0.68
2006/07 2008/09 -

0.48
0.42
0.48
0.00
0.47
0.06
0.10
0.39

PWA

65,63843,743- 100,771
82,15543,0370.02 88,274
0.43 59,961
0.26 45,177
0.90 26,418
0.61 97,563

CV(PWA)

0.353
0.376
0.254
0.300
0.286
0.255
0.244
0.229
0.188
0.193

Area VIWWSSV WSVC WSVP PWA CV(PWA)
0.84 0.16
9,614
0.220
0.75 0.25
5,566
0.367
0.20 0.03 0.77 12,404 0.615
0.92 0.05 0.03 44,801 0.582
0.95 0.01 0.05 18,927 0.355
0.62 0.38 29,261 0.379
0.10 0.90 9,406
0.291

Year

1996/97
1998/99
2000/01
2002/03
2004/05
2006/07
2008/09

26

WSSV WSVC WSVP

0.96
0.27
0.87
0.73
0.40
-

0.04
0.73
0.11
0.10
0.29
0.76
0.17

0.02
0.17
0.31
0.24
0.83

PWA

CV(PWA)

7,530
20,166
12,571
10,543
14,843
17,20626,364

0.258
0.280
0.257
0.218
0.228
0.556
0.226

SC/F14/J3

Table 12. Variance–covariance matrices for the logarithm of minke whale abundance estimates whenweighted over survey modes for Areas IIIE, IV, VW, VE and VIW.
Area IIIE
1995/960.501

1997/98

1999/00

2001/02

1995/96

2003/04

2005/06

2007/08

1997/98
1999/00
2001/02-0.018-0.018-0.017

0.604
0.045
0.066

0.220
0.470

0.604

2003/04
2005/06-0.0230.005

0.064-0.0180.020-0.015

0.077
0.028

0.556
0.003

0.409

2007/08-0.029

0.008

0.004

-0.006

0.025

0.038

0.477

1989/90

1991/92

1993/94

1995/96

1997/98

1999/00

2001/02

Area IV
2003/04

2005/06

1989/900.245

1991/92-0.0170.418

1993/94
1995/96-0.002-0.003-0.008
0.004

0.202
0.013

0.190

1997/98

0.008-0.009

0.008

0.005

0.258

1999/00

0.003

0.002

-0.001-0.008-0.006

0.108

2001/02
2003/04

0.004-0.0130.014-0.0160.009-0.018-0.001-0.004-0.006-0.001-0.002
0.002

0.149
0.013

0.382

2005/06-0.031

0.005

0.017

0.009

-0.011

0.020

0.030

-0.0110.231

2007/08

0.005

0.047

0.003

0.026

-0.002-0.006

0.015

0.000

-0.048

2007/08

0.348

Area VW
1990/91
1992/93
1994/95
1996/97
1998/99
2000/01
2002/03
2004/05
2005/06
2006/07
2007/08
2008/09

1990/91 1992/93 1994/95 1996/97 1998/99 2000/01 2002/03 2004/05 2005/06 2006/07 2007/08 2008/09
0.286
0.017
0.330
-0.035 -0.043
0.219
0.044
0.007
0.024
0.197
0.019
0.014
-0.0270.042
0.393-0.076 -0.079
0.053-0.095 -0.007
0.798
0.039
-0.024 -0.016
0.017
0.040
0.010
0.119
-0.022 -0.036
0.038-0.0070.007-0.036 -0.010
0.145
0.039
-0.041
0.111-0.054
0.062
0.247
0.035
0.020
0.392
0.068
0.048
0.008
-0.0340.031-0.046 -0.035 -0.002 -0.050
0.274-0.015 -0.053
0.020
0.018
-0.027 -0.046 -0.009
0.016
0.033
-0.071
0.238
0.004
0.105
0.003
0.029
0.004
-0.022 -0.031
0.002-0.073 -0.012
0.070
0.258

Area VE
1990/91
1992/93
1994/95
1996/97
1998/99
2000/01
2002/03
2004/05
2006/07
2008/09

1990/91 1992/93 1994/95 1996/97 1998/99 2000/01 2002/03 2004/05 2006/07 2008/09
0.305-0.0070.335-0.0260.009
0.212-0.037 -0.001 -0.035
0.221
0.007
-0.0320.067-0.0300.170-0.0320.019-0.010
0.017
0.003
0.184
-0.0330.006-0.029 -0.001
0.007-0.0150.137-0.002
0.057
0.025
0.011
0.060
-0.014 -0.001
0.117
0.009
-0.0120.007-0.0350.021-0.037
0.037
0.008
0.144
-0.038 -0.026 -0.021
0.023
0.032
-0.007 -0.007 -0.015
0.024
0.153

Area VIW
1996/97 1998/99 2000/01 2002/03 2004/05 2006/07 2008/09
1996/97

0.280

1998/99
2000/01
2002/03-0.004
0.016
0.023

0.884
0.001
-0.002

0.130
0.051

0.254

2004/05
2006/07-0.0240.012-0.046-0.040

0.011
0.120

0.015
0.005

0.229
-0.0220.207

2008/09

0.074

0.015

0.054

-0.0270.020-0.0500.170

27SC/F14/J3

Table 13. The values of g(0) predicted by the regression model of equation (21) and mean school size E(s)for the various JARPA and JARPA II strata. Instances where these fall below or above the lowest (0.405)
or highest (0.794) OK values used in developing the regression relationship are shown in italics. Whencalculating g(0) adjusted abundance estimates for the JARPA and JARPA II using the value below, thosevalues in italics have been replaced by the appropriate bounding OK value. Note also, as specified in thetext, that values of E(s) below that are greater than 8 were set equal to 8 when applying the regression
model to calculate g(0).
Area IV SSVStratag(0)8990NW 0.527
8990NE 0.486
8990SW 0.606
8990SE 0.577
8990PB 0.611
9192NW 0.485
9192NE 0.405
9192SW 0.613
9192SE 0.616
9192PB 0.581
9394NW 0.525
9394NE 0.490
9394SW 0.538
9394SE 0.564
9394PB 0.635
9596NW 0.502
9596NE 0.471
9596SW 0.594
9596SE 0.532
9596PB 0.620
9798NW 0.405
9798NE 0.452
9798SW 0.543
9798SE 0.592
9798PB 0.551
9900NW 0.524
9900NE 0.526
9900SW 0.511
9900SE 0.687
9900PB 0.492
0102NW 0.518
0102NE 0.405
0102SW 0.595
0102SE 0.559
0102PB 0.518
0304NW 0.525
0304NE 0.476
0304SW 0.526
0304SE 0.589
0304PB 0.626

E (s )
1.321
2.250
1.988
2.700
1.767
1.729
2.208
2.271
1.842
2.507
1.375
1.767
2.266
2.843
2.049
2.344
1.882
2.268
2.535
1.465
2.632
1.708
2.011
1.742
3.065
1.208
1.314
3.865
8.737
2.821
1.368
2.027
1.741
2.036
3.077
1.373
1.574
2.717
1.856
1.966

Area IV SVC
Stratag(0)9192SW 0.556
9192SE 0.563
9192PB 0.593
9394NW 0.520
9394NE 0.501
9394SW 0.461
9394SE 0.530
9394PB 0.626
9596NW 0.508
9596NE 0.471
9596SW 0.595
9596SE 0.502
9596PB 0.583
9798NW 0.505
9798NE 0.520
9798SW 0.621
9798SE 0.613
9798PB 0.614
9900NW 0.552
9900NE 0.516
9900SW 0.607
9900SE 0.608
9900PB 0.556
0102NW 0.512
0102NE 0.552
0102SW 0.624
0102SE 0.628
0102PB 0.623
0304NW 0.543
0304NE 0.552
0304SW 0.538
0304SE 0.605
0304PB 0.636
0506NW 0.687
0506NE 0.687
0506SW 0.520
0506SE 0.520
0506PB 0.575
0708NW 0.587
0708NE 0.587
0708SW 0.493
0708SE 0.493
0708PB 0.587

Area V SSVStratag(0)9091NW 0.500
9091NE 0.590
9091SW 0.573
9091SE 0.530
9293NW 0.605
9293NE 0.594
9293SW 0.481
9293SE 0.585
9495NW 0.563
9495NE 0.581
9495SW 0.549
9495SE 0.575
9697NW 0.592
9697NE 0.592
9697SW 0.540
9697SE 0.459
9899NW 0.595
9899NE 0.567
9899SW 0.522
9899SE 0.586
0001NW 0.572
0001NE 0.541
0001SW 0.456
0001SE 0.470
0203NW 0.568
0203NE 0.575
0203SW 0.463
0203SE 0.504
0405NW 0.574
0405NE 0.583
0405SW 0.667
0405SE 0.429

E (s )
3.543
2.769
5.805
1.280
1.650
2.700
3.328
1.545
2.353
2.222
2.800
3.322
1.960
2.824
1.800
1.806
1.806
1.806
1.381
1.381
2.600
6.140
4.917
1.627
1.627
1.627
1.627
1.627
3.710
3.710
3.710
3.710
3.710
10.50010.500

2.000
2.000
2.423
1.975
1.975
1.975
1.975
1.975

28

E (s )
2.277
2.167
2.706
2.429
1.500
1.543
3.613
2.342
2.414
1.631
2.780
2.256
1.683
1.903
3.079
3.101
1.865
2.744
4.575
2.235
1.872
1.984
3.038
3.365
2.189
1.931
3.196
2.691
2.200
1.543
8.391
2.393

Area V SVC
Stratag(0)9293NW 0.600
9293NE 0.669
9293SW 0.518
9293SE 0.478
9495NW 0.483
9495NE 0.579
9495SW 0.520
9495SE 0.507
9697NW 0.496
9697NE 0.578
9697SW 0.629
9697SE 0.592
9899NW 0.584
9899NE 0.558
9899SW 0.497
9899SE 0.706
0001NW 0.627
0001NE 0.618
0001SW 0.557
0001SE 0.591
0203NW 0.589
0203NE 0.603
0203SW 0.576
0203SE 0.579
0405NW 0.627
0405NE 0.592
0405SW 0.595
0405SE 0.551
0506NW 0.480
0506SW 0.479
0607NW 0.515
0607SW 0.577
0607NE 0.515
0607SE 0.577
0708NW 0.505
0708SW 0.503
0809NW 0.466
0809SW 0.465
0809NE 0.466
0809SE 0.465

E (s )
1.565
8.059
4.479
3.535
3.627
1.958
3.875
4.239
3.929
1.933
7.100
6.223
3.500
2.150
4.143
4.143
6.118
6.118
1.727
2.250
2.231
1.636
2.000
1.931
1.526
1.526
2.357
2.219
3.559
3.559
2.846
1.857
2.846
1.857
3.558
3.558
3.236
3.236
3.236
3.236

SC/F14/J3

Table 13 (Cont).
Area IIIE SSVStratag(0)9596IIIE 0.634
9798IIIE 0.621
9900IIIE 0.560
0102IIIE 0.487
0304IIIE 0.576

E (s )
1.459
1.673
2.074
2.858
2.594

Area IIIE SVC
Stratag(0)9596IIIE 0.665
9798IIIE 0.665
9900IIIE 0.635
0102IIIE 0.531
0304IIIE 0.636
0506IIIE 0.582
0708IIIE 0.606

Area VIW SSVStratag(0)9697VIW 0.534
9899VIW 0.602
0001VIW 0.489
0203VIW 0.610
0405VIW 0.581

E (s )
1.000
1.000
3.000
3.889
2.241
2.667
1.975

E (s )
2.322
1.371
3.660
1.200
1.634

Area VIW SVC
Stratag(0)9697VIW 0.564
0001VIW 0.595
0203VIW 0.590
0405VIW 0.613
0607VIW 0.520
0809VIW 0.595

E (s )
2.111
1.400
1.500
1.286
2.846
1.556

Table 14. Minke whale abundance estimates when weighted over survey modes for the various sensitivity
tests. Percentage changes are relative to the base case. For the base case the CV of each estimate is shownin parentheses.
Area IV
Year
Base case
Trackline option B

1989/90

1991/92

1993/94

1995/96

1997/98

1999/00

2001/02

2003/04

2005/06 2007/08

29,99332,41827,59832,97016,562

44,94530,80732,97063,79415,088(0.527)(0.720)(0.473)(0.458)(0.542)(0.338)(0.402)(0.682)(0.509)(0.645)

Average ofchange-26,13528,58920,941

25,624

15,78943,122

46,577

24,79520,74817,518--12.9%-11.8%-24.1%-22.3%

-4.7%

-4.1%

51.2%

-24.8%-67.5%

16.1%-10.5%

Trackline option C

28,94630,43028,98528,86019,326

45,11554,06653,07525,81020,969-Alternative detection functions-3.5%
31,136-6.1%
34,9495.0%
29,606-12.5%
28,722

16.7%
28,3200.4%
71,56375.5%
50,111

61.0%
42,844-59.5%
28,98739.0%
25,052

11.6%-

Gap density = 0

3.8%
29,0537.8%
31,062

7.3%
26,082-12.9%
26,40471.0%
16,24959.2%
53,85862.7%
48,35029.9%
28,322-54.6%
25,50866.0%
19,78624.0%-

Gap density = stratum to the south-3.1%
24,165-19.4%-4.2%
24,539-24.3%-5.5%
24,723-10.4%-19.9%
32,741-0.7%-1.9%
17,8317.7%

19.8%
55,44923.4%

56.9%
42,34837.5%-14.1%
29,696-9.9%-60.0%
30,394-52.4%

31.1%
21,573
43.0%

-0.1%
-0.6%

Extrapolation for incomplete coverage

29,08531,60324,86931,441

16,33953,23549,91230,55826,373

20,521

-

-3.0%

-2.5%

-9.9%

-4.6%

-1.4%

18.4%

62.0%

-7.3%

-58.7%

36.0%

2.9%

-

-

24,735-10.4%

29,692-9.9%

16,288-1.7%

56,98226.8%

50,81064.9%

30,333-8.0%

27,832-56.4%

21,31641.3%

5.8%

50,73669.2%

55,87872.4%

44,12759.9%

55,02466.9%

30,585
84.7%

95,725113.0%

91,987
198.6%

53,26061.5%

48,570-23.9%

41,094172.4%

87.5%

Skipping correction(Ignoring the first two surveys)
g(0) adjustment

Area VYear
Base case1990/91 1992/93 1994/95 1996/97 1998/99 2000/01 2002/03 2004/05 2006/07 2008/09

104,013 99,680 118,779 106,991 151,072 74,030 67,661 109,173(0.458) (0.461) (0.515) (0.523) (0.326) (0.336) (0.308) (0.523)
108,256 160,110 135,547 108,120 124,819 81,913 73,605 94,958
4.1%
60.6% 14.1%
1.1% -17.4% 10.6% 8.8% -13.0%
9.4%
124,378 162,693 145,831 117,597 135,090 77,014 73,602 98,742
19.6% 63.2% 22.8%
9.9% -10.6% 4.0%
8.8%
-9.6%
16.4%
162,192 163,258 103,951 124,455 140,249 83,024 94,667 106,329
55.9% 63.8% -12.5% 16.3% -7.2% 12.1% 39.9% -2.6%
18.8%
114,884 142,237 124,170 103,468 118,477 94,021 65,730 95,561
10.5% 42.7% 4.5%
-3.3% -21.6% 27.0% -2.9% -12.5%
7.0%
131,234 91,093 107,463 110,894 107,607 66,138 54,470 93,220
26.2% -8.6% -9.5%
3.6% -28.8% -10.7% -19.5% -14.6%-7.7%
224,784 151,466 221,444 299,433 255,613 207,068 237,176 132,431 139,878 200,831
123.1% 91.9% 112.9% 200.4% 115.2% 93.5% 57.0% 78.9% 106.7% 84.0% 109.1%

100,745(0.445)Trackline option B
122,928
22.0%
Trackline option C
146,733
45.6%
Alternative detection functions
116,939
16.1%
Extrapolation for incomplete coverage 126,701
25.8%
Skipping correction
g(0) adjustment

Averageof change

78,919(0.371)
81,716
3.5%
86,866
10.1%
83,776
6.2%
78,546-0.5%

29

SC/F14/J3

Table 15. Estimated annual rate of increase in minke whale abundance (α), together with their standard
errors and 95% confidence intervals, as provided by the log-linear model selected by AICc for the basecase and sensitivities for Areas IIIE+IV and V+VIW. σ is the standard derivation of the ‘model error’
distribution associated with the logarithms of the abundance estimates. Base_P: Base case (based onabundance), Base_D: Base case but using density instead of abundance, TB: Trackline option B, TC:
Trackline option C, DF: Alternative detection function, G0: Density in Gap =0, GB: Density in Gap is as
in stratum to the south, IC: Interpolation in incomplete coverage area, SK: Omit years when skipping
occurred.
a) Areas IIIE + IV
Sensitivity
Base_P
Base_D
TB
TC
DF
G0
GB
IC
SK
g(0) adjusted

α
0.011
0.010
0.014
0.028
0.031
0.001
0.034
0.026
0.037
0.025

SE (α )

α95%LL

α 95%UL

σ

0.017
0.016
0.020
0.019
0.019
0.022
0.019
0.019
0.024
0.019

-0.023-0.022-0.025-0.010-0.006-0.042-0.005-0.011-0.010-0.013

0.045
0.042
0.053
0.065
0.069
0.045
0.072
0.063
0.085
0.063

0.755
0.688
0.745
0.699
0.700
0.556
0.659
0.690
0.725
0.717

SE ( σ )
0.069
0.064
0.068
0.065
0.065
0.075
0.063
0.065
0.070
0.066

Selected modeli)i)iii) with T3iii) with T3iii) with T3iii) with T3iv) with T3iii) with T3iii) with T3iii) with T3

α
0.006
0.004
-0.006-0.017-0.005-0.009-0.013-0.006SE (α )

α 95%LL

α 95%ULσ

SE (σ )

0.014
0.013
0.015
0.015
0.016
0.015
0.020
0.016

-0.022-0.021-0.038-0.047-0.038-0.040-0.055-0.039

0.033
0.034
0.025
0.013
0.028
0.023
0.028
0.026

0.575
0.509
0.430
0.409
0.458
0.415
0.498
0.430

0.059
0.055
0.055
0.055
0.054
0.054
0.061
0.057

Selected modeli)i)iv) with T3iv) with T3iv) with T3iv) with T3iv) with T4iv) with T3b) Areas V+VIW
Sensitivity
Base_P
Base_D
TB
TC
DF
IC
SK
g(0) adjusted

Figure 1. Stratification of the JARPA and JARPA II research area. Area IIIE and VIW were stratified into
northern and southern strata in the JARPA II period.

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SC/F14/J3

Figure 2a. Primary searching effort (thin lines) and associated primary sightings of minke whales (pinkcircle) in Area IV (70oE-130oE) together with the ice edge (dotted blue line) from 1989/90 to 2007/08
JARPA and JARPA II surveys.

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SC/F14/J3

Figure 2b. Primary searching effort (thin lines) and associated primary sightings of minke whales (pinkcircle) in Area VW (130oE-165oE)together with the ice edge (dotted blue line) from 1990/91 to 2008/09
JARPA and JARPA II surveys.

32

SC/F14/J3

Figure 2b (Cont.).

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SC/F14/J3

Figure 2c. Primary searching effort (thin lines) and associated primary sightings of minke whales (pinkcircle) in Areas VE and VIW (165oE-145oW)together with the ice edge (dotted blue line) from 1990/91 to
2008/09 JARPA and JARPA II surveys.

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Figure 2c (Cont.).

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SC/F14/J3

Figure 2d. Primary searching effort (thin lines) and associated primary sightings of minke whales (pinkcircle) in Area IIIE (35oE-70oE) together with the ice edge (dotted blue line) from 1995/96 to 2007/08
JARPA and JARPA II surveys.

36

SC/F14/J3a) Areas IIIE, IV and part of V combined (35oE-175oE).
1989/90
IV-NW IV-NE
5
2
IV-SW IV-SE
3
1
IV-PB
4

1991/92
IV-NW IV-NE
5
1
IV-SW IV-SE
3
2
IV-PB
4

1993/94
IV-NW IV-NE
5
2
IV-SW IV-SE
3
1
IV-PB
4

1995/96
IV-NW IV-NE
III-E
3
5
1
IV-SW IV-SE
2
6
IV-PB
4

1997/98
IV-NW IV-NE
III-E
2
4
1
IV-SW IV-SE
5
3
IV-PB
6

1999/00
IV-NW IV-NE
III-E
2
3
1
IV-SW IV-SE
5
4
IV-PB
6

2001/02
IV-NW IV-NE
III-E
2
3
1
IV-SW IV-SE
5
4
IV-PB
6

2003/04
IV-NW IV-NE
III-E
2
3
1
IV-SW IV-SE
5
4
IV-PB
6

2005/06
III-NE IV-NW IV-NE V-NW V-NE
3
4
6
2
1
III-SE IV-SW IV-SE V-SW V-SE
3
4
6
2
1
IV-PB
5

2007/08
III-NE IV-NW IV-NE V-NW
V-NE
1
2
3
4
unsurveyed
III-SE IV-SW IV-SE V-SW V-SE
1
2
3
4
unsurveyed

IV-PB
unsurveyedb) Areas V and VIW combined (130oE-145oW)
1990/91
V-NW V-NE
2
4
V-SW V-SE
1
3

1992/93
V-NW V-NE
3
1
V-SW V-SE
2
4

1994/95
V-NW V-NE
2
3
V-SW V-SE
1
4

1996/97
V-NW V-NE
VI-W
4
2
1
V-SW V-SE
3
5

1998/99
V-NW V-NE
VI-W
2
1
5
V-SW V-SE
3
4

2000/01
V-NW V-NE
VI-W
4
2
1
V-SW V-SE
5
3

2002/03
V-NW V-NE
VI-W
4
2
1
V-SW V-SE
5
3

2004/05
V-NW V-NE
VI-W
4
2
1
V-SW V-SE
5
3

2006/07
V-NW V-NE VI-NW
42
1
V-SW V-SE VI-SW
4
3
1

2008/09
V-NW V-NE VI-NW
1
42
V-SW V-SE VI-SW
1
3
2

Figure 3. Survey order by strata for the JARPA and JARPA II cruises from 1989/90 to 2008/09. Key:
III=Area III, IV=Area IV, V=Area V, VI=Area VI, E=East, W=West, NW=North-West, NE=North-East,
SW=South-West, SE=South-East, PB= Prydz Bay. Common number in a season indicates that two strata
were surveyed in a same period. V-NE, V-SE and IV-PB strata could not be surveyed at all in 2007/08
season.

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Figure 4. Start and end dates of JARPA II surveys (2005/06-2008/2009) in the survey area.

Figure 5. The values predicted by the regression relationship of equation (21) for g(0) by block for the
IDCR-SOWER surveys in Areas III, IV, V and VI are shown plotted against the estimates obtained bycircumpolar cruise (CPII) are shown by (o) and those from CPIII by (+). A 45o line is added to show
where points would reflect exact agreement.

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SC/F14/J3

Areas IIIE and IV

Areas V and VIWFigure 6a. Histograms of the smeared perpendicular distance (in n.miles) distributions of minke schoolsightings with fitted detection functions for each stratum in Areas IIIE, IV, V and VIW for SVC data. n is
the number of the sightings used in estimation of the detection function.

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SC/F14/J3

Areas IIIE and IV

Areas V and VIWFigure 6b. Histograms of the smeared perpendicular distance (in n.miles) distributions of minke schoolsightings with fitted detection functions for each stratum in Areas IIIE, IV, V and VIW for SVP data. n is
the number of the sightings used in estimation of the detection function.

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SC/F14/J3

Area IV

Area V

180,000

350,000

160,000

300,000

140,000
250,000

120,000
100,000

200,000

80,000150,000

60,000100,000

40,00050,00020,0000
1989/901989

01994/95
1994

1999/00
1999

2004/05
2004

2009/102009

1989
1989/901994
1994/95

1999
1999/00

2004
2004/052009
2009/10Figure 7. The base case estimates of annual abundance from Table 14 together with their 95% CIs arecompared to exponential trend estimated by the AICc-selected model i) of equation (7) for Areas IV (leftpanel) and V (right panel).

Area IV
250,000
200,000
150,000
100,000
50,00001985/86
1985

1990/91
1990

1995/96
1995

2000/01
2000

1990/91
1990

1995/96
1995

2000/01
2000

2005/06
2005

Area V
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,0000
1985/86
1985

2005/06
2005

2010
2010/11

Figure 8. Plots as for Figure 7, but with the abundance estimates and associated exponential model for thebase case replaced by the corresponding g(0)-adjusted results. The IDCR-SOWER estimates for acommon northern boundary for CPII and CPIII as agreed by the 2012 IWC SC meeting are shown by theopen triangles (IWC, 2013); their confidence intervals include allowance for additional variance, as do
those for the JARPA and JARPA II surveys. The dashed curves indicate the 95% CIs for the exponential
model.

41