Technical Reports of the Institute of Cetacean Research (2017)

pp. 28–36

Technical Report (not peer reviewed)

Sighting survey procedures for abundance estimates of large whales
in JARPA and JARPAII, and results for Antarctic minke whales
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

ABSTRACTThis paper provides an overview of the sighting surveys and analytical procedures for abundance estimates oflarge baleen whales under the non-lethal component of JARPA/JARPAII. The paper also summarizes the abundance estimates and abundance trends for Antarctic minke whales based on sighting data collected by JARPA
and JARPAII surveys between 1989/90 and 2008/09.

INTRODUCTION
There are two main sources of sighting data for abundance estimates of large whales in the Antarctic. One
is the International Decade for Cetacean Research(IDCR)/Southern Ocean Whale and Ecosystem Research(SOWER) program (Matsuoka et al., 2003), and the otheris the sighting surveys conducted as part of the non-lethal
component of the Japanese Whale Research Programs in
the Antarctic (JARPA, JARPAII and NEWREP-A).
One of the features of the sighting surveys under theJapanese whale research programs is that, unlike the
IDCR/SOWER program, surveys are repeated in the same
area and in the same months every second austral summer season, over a long period. Therefore, those sighting
surveys facilitate both estimations of abundance trends
and the extent of inter-year variability in local abundance
in a particular area.
The objective of this paper was to outline the sighting procedures adopted during JARPA/JARPAII and theanalytical procedures used to estimate whale abundance
and abundance trends from sighting data. A summary of
the results for Antarctic minke whales based on the JARPA
and JARPAII sighting data is also presented. Surveys andanalytical procedures used took into consideration therecommendations from the Scientiﬁc Committee (SC) of
the International Whaling Commission (IWC) (see IWC
2008, pp. 349).

MATERIALS AND METHODS
Survey area and geographical stratiﬁcation
The main research areas were IWC Management Areas
IV (70°E–130°E) and V (130°E–170°W) until 1994/95.
28

Since 1995/96 the sighting surveys also covered Areas
IIIE (35°E–70°E) and VIW (170°W–145°W), south of 60°S.
Each of these Areas was divided into smaller strata as
shown in Figure 1. The surveys were conducted fromthe end of December to March in each austral summer
season, with the surveys in Areas IV and V concentrated
in January and February in most years, which coincides
with the peak period for migration of Antarctic minke
whales to their Antarctic feeding grounds (Kasamatsu
et al., 1996). The starting and ending dates in JARPA/JARPAII surveys are shown in Figure 2.
Research vessels
JARPA/JARPAII comprised a combination of sighting
and sampling surveys, and several specialized vessels
participated in the surveys as Sighting Sampling Vessels (SSVs) and Sighting Vessels (SVs). Kyo-Maru No. 1,
Toshi-Maru No. 25 and Toshi-Maru No. 18 operated from
1989/90. Kyoshin-Maru No. 2 operated since 1995/96.
Yushin-Maru operated since the 1998/1999 survey as thereplacement of Toshi-Maru No. 18. Yushin-Maru No. 2 operated since the 2001/2002 survey as the replacement ofToshi-Maru No. 25. Yushin-Maru No. 3 operated since the
2007/2008 survey as the replacement of Kyo-Maru No. 1.
Kaiko-Maru operated from 2005/06 to 2008/09 surveys.
Details of the vessels were provided in Matsuoka et al.
(2011) and Hakamada et al. (2013).
Line transect method
Tracklines are designed randomly or systematically sothat they are independent from distribution of the objects (e.g., whales). Tracklines consist of parallel lines orzigzag lines as shown in Figure 3. In shipboard or aerialSighting survey procedures for abundance estimates of large whales in JARPA and JARPAII, and results for Antarctic minke whales

Figure 1.

Research area for the sighting surveys under the JARPA/JARPAII. Dotted lines indicate 45n.mile lines fromthe ice edge lines.

Figure 2.

Starting and ending dates of JARPA/JARPAII sighting surveys for abundance estimation of large whales inAreas IV and V.

Figure 3.

Example of zigzag lines used during a line transect
survey.

surveys, there is signiﬁcant cost for travelling from one
transect to another. In such circumstance, zigzag lines(or saw-tooth line) are used to save costs (Buckland
et al., 2015).
During the line transect survey, observers on the
research vessels search for whale schools from the platforms on the research vessels (Figure 4). When a whale
school is detected, the distance between the school andFigure 4.

Platforms of observation of whale schools in specialized vessels used during JARPA and JARPAII.

the observer (r), and the angle from the track line (θ) arerecorded (Figure 5). Position of the research vessel andtime of the detection are also recorded.
After the detection of a whale school, in principle, thevessels approach the school to conﬁrm the species and29

Technical Reports of the Institute of Cetacean Research (2017)

school size. Activities of the research vessel are recorded
to calculate distance and time on-eﬀort. Weather (e.g.,
air temperature, sea surface temperature, Beaufort scale,visibility etc.) are recorded every hour during sighting
surveys to record the weather condition that could potentially aﬀect the detectability.
Trackline design and sighting procedures in JARPA/
JARPAII
Survey design and procedures used under JARPA and
JARPAII fundamentally followed the ʻRequirements
and Guidelines for Conducting Surveys and Analyzing
Data within the Revised Management Scheme (RMS)ʼ
(IWC, 2012). More details on trackline design and sighting
procedures in JARPA and JARPAII are found in Hakamada
et al. (2013) and Hakamada and Matsuoka (2014a). The
Sighting Vessels (SVs) conducted the surveys in closing

Figure 5.

Illustration of the detection of whales from observers on research vessels. Black hexagon indicatesa research vessel, white circle indicates a whale
school, horizontal line indicates a trackline, r is distance to the whale school from the research vessel,
θ is an angle from the track line and x is perpendicular distance from the trackline.

Figure 6.

30

mode (SVC) up to and including 1996/97. Surveys inpassing mode (SVP) started in 1997/98 because previous
studies (Butterworth and McQuaid, 1986; Haw, 1991)
had shown potential eﬀect on abundance estimate of thesurvey mode.
The trackline was designed to cover the whole research area and the design was consistent throughout
the JARPA and JARPAII surveys. The starting points wereselected at random from 1n.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 45n.miles from that edge insouthern strata, and on this locus and the 60°S latitude
line in the northern strata.
There were two modiﬁcations in trackline design in
JARPAII surveys in relation to the previous JARPA, which
responded to recommendations from the IWC SC to
improve abundance estimation. One is that a saw-toothtype trackline for the southern strata was not used. The
other is that the northern and southern strata weresurveyed in the same period (Nishiwaki et al., 2014). As
noted above, JARPA/JARPAII comprised a combination ofsighting and sampling surveys using SSVs and SVs. SSVs
and SVs surveyed the area independently. An example
plot of sightings on tracklines actually surveyed is shownin Figure 6.
Smearing parameters and truncation distanceThe data recorded for radial distance and angle aresmeared using the Method II of Buckland and Anganuzzi(1988). The smearing parameter values were estimatedfor Antarctic minke (Hakamada et al., 2013; Hakamada
and Matsuoka, 2014a) whales. After the smearing, theperpendicular distances are truncated at 1.5n.miles for
Antarctic minke whales. This treatment is the same as

Primary searching eﬀort (thin lines) and associated primary sightings of Antarctic minke whales (pinkcircles) in Area IV together with the ice edge (dotted blue line) in the 2005/06 JARPAII survey.

Sighting survey procedures for abundance estimates of large whales in JARPA and JARPAII, and results for Antarctic minke whales

the one employed in abundance estimation from the IWC
IDCR/SOWER surveys (Branch and Butterworth, 2001a;
Branch, 2006). The number of sightings remaining aftersmearing and truncation includes sightings with both
conﬁrmed and unconﬁrmed school sizes.
Correction of observed angle and distance
To be able to correct for bias in distance and angle estimation, a distance and angle estimation experiment
was conducted on each vessel each year (Nishiwaki
et al., 2014). The correction factors estimated in Matsuoka et al. (2011) and Hakamada and Matsuoka (2014b)
were used for these analyses. More details of the methodology for the estimation of the correction factors arefound in Burt and Stahl (2000).
Abundance estimation
The methodology for abundance estimation used in thisstudy is described by Branch and Butterworth (2001a;
2001b) and Branch (2006), and has been termed theʻstandard methodologyʼ in the IWC SC terminology. The
program DISTANCE ver. 6.0 (Thomas et al., 2010) wasused to provide abundance estimates corresponding toeach trackline. The following equation was used for abundance estimation in each stratum:Pi =AE ( s)ni
2wLi

(1)

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

L P
i i

P=

i

(2)

L
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 
2
2
CV ( P ) = CV    +{CV ( E ( s ))} +{CV ( w)} (3)
L





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 g ( y ) = 1 − exp  −   a   (4)

where y is perpendicular distance, and a>0 and b≥1 areparameters of the model to be estimated. It is assumed
here that g(0)=1 (i.e. the detection probability of a schoolon the track line is 1). Detections with perpendiculardistances of more than 1.5 n.miles were truncated whenestimating eﬀective search half-width (ESW) w. More details of this detection function are given in Buckland et al.
(1993; 2001).
Stratiﬁcation of data to estimate ESW
In line with an IWC (2008) recommendation, ESWs were
estimated by stratum. In cases where the sample size wassmaller than 15, the sighting data were pooled among
strata to estimate the detection function in line withother IWC (2008) recommendations. In such cases, data
were pooled across West-East strata because sighting
conditions and school size distributions are expected tobe more similar than for North-South strata. In instanceswhere there were less than 15 detections in southern/
northern strata, data were aggregated over the whole ofeach Area.
Estimated mean school size
In line with an IWC (2008) recommendation, meanschool 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. More speciﬁcally, 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 sizefor sightings within the truncated distance was substituted instead in the equation (1). If the consequent meanschool 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 statisticallysigniﬁcant at this 15% level. Similarly to the analyses for
the IDCR/SOWER data (Branch and Butterworth, 2001a;
Branch 2006), for SVP the mean school size estimatedfrom SVC data was used instead of estimating this from
SVP data, for which school size estimates are known tobe negatively biased as a result of not approaching all31Technical Reports of the Institute of Cetacean Research (2017)

schools closely (Butterworth and McQuaid, 1986).
Combined encounter rate taking account of correlation
among two or three SSV track lines
The survey by the SSVs comprised two or three parallel tracklines. There may be a positive correlation in theencounter rates along these lines, which would cause anegative bias in the estimate of the CV of the overall encounter rate if the results from each vessel were assumed
to be independent. To take this possible covariance into
account, the CV of the encounter rate when combinedover the two or three SSVs with their parallel tracklines
was estimated as:n
Var  n
LCV   =
n
L
L

(5)

where
ni =

n , L =L
i, j

i

j

i, j

j

with ni,j and Li,j being the number of primary sightings ofminke whale schools and the primary eﬀort on the i thtransect as surveyed on the j th tracklines. The varianceof (n/L) is calculated as:2k1  Li   ninn
Var   =
 L  L − L
L
k
−
1
(
)
i
  i=1
  




2

(6)

where k is the number of transects on each trackline.
Estimating abundance trend
In order to examine the potential eﬀect of survey timing
and that of survey mode, the four models shown below
were considered.
Model i) log { Pobs ( y, a)} =log { ptrue (0, a )} + αy + ε y ,a + ηy ,a(7)Model ii) log { Pobs ( y, a)} =log { ptrue (0, a )} + αy + M + ε y ,a + ηy ,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 + ηy ,a (10)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 the32

underlying abundance (i.e. free from the eﬀect of surveymode) which is to be estimated in year 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 a and η y,a is a normally
distributed error with mean of 0 and variance of σ 2 associated with “model error.”
The middle day of the survey period in each stratum
was calculated and categorized into groups as a basis tospecify T. Because the estimate of trend α might be sensitive to the deﬁnition of T, four grouping were considered:
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. the eﬀectof those groups is set to zero in the calculations). T1–T4
were used as categorical covariates in Models iii) and iv)(equations (9) and (10)) above. The best grouping wasselected by comparing the corrected AIC (AICc) (Sugiura,
1978; Hurvich and Tsai, 1989; 1991), which can be applied to linear models with normal errors, and is used instead of Akaike Information Criterion (AIC) (Akaike, 1973)
for each model.
Correction of nominal abundance estimates and theirvariance-covariance matrices
Using the estimated coeﬃcients of models i)–iv), nominal
abundance estimates for each survey mode can be corrected, and the weighted average of the corrected abundance estimates for each mode were calculated. Amongthe weighted average of the abundance estimates, thatusing correction factors based on the best model istreated as ʻBase case.ʼ Further details are provided in
Hakamada et al. (2013).
Sensitivity analyses
There are various possible sources of bias in abundance
estimates and their trends, the more important of whichwere discussed at the IWC SC meeting in 2007 (IWC,
2008; Table 1). In order to examine their possible magnitudes, sensitivity analyses to examine robustness ofabundance estimates and trend were conducted.

Sighting survey procedures for abundance estimates of large whales in JARPA and JARPAII, and results for Antarctic minke whales
Table 1
List of the factors for which the sensitivity of abundance estimates and/or trends is examined. Speciﬁcations are given for both thebase case and the sensitivities, with more details provided in Hakamada et al. (2013) and Hakamada and Matsuoka (2014a).
Sensitivity factors

Speciﬁcation for the base caseSpeciﬁcation for sensitivities

Estimation by stratum, except that For SSVs, ESW averaged over vessels concerned wasused. For SVs the detection function estimation
when sample size is less than 15,
takes account covariates.
strata are pooled.
Trackline following ice edge Complete tracklines used.
(1) Exclude trackline segments along the ice edge
contours(Option B). (2) Use only transects parallel to lines oflongitude (Option C).
Abundance in gaps between Assume same density as in stratum to (1) Assume the density is 0. (2) Assume the same dennorthern and southern strata
the north.
sity as in the stratum south.
Interpolation of density in the Estimated density assumed to apply Extrapolate based on average ratio of density in theto complete stratum for JARPA.
unsurveyed area within aunsurveyed to surveyed area as estimated in other
Interpolation using GLM was usedstratumyears with complete coverage for JARPA and JARPA II.
for JARPAII.
ʻSkippingʼ
Assumed not to introduce bias.
Exclude the abundance estimates for years when
ʻSkippingʼ occurred when estimating trends.
g(0)Assumed to equal 1.
Adjust for g(0) estimates provided by the regression
model.
Shoulder of detection function

RESULTSThis section presents the results of abundance estimate
for Antarctic minke whale.
Abundance estimate
Nominal abundance estimates by strata in Areas IIIE, IV, V
and VIW in each survey mode (SSV, SVC and SVP) during
JARPA/JARPAII were provided in Hakamada et al. (2013)and Hakamada and Matsuoka (2014a). Detection functions to estimate ESW for abundance estimation werealso provided by these authors. Hakamada and Matsuoka(2014a) shows interpolated abundance estimate for strata where survey coverage was incomplete. 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. (2013).
Log-linear models to estimate abundance trend
Model i) was selected for both Areas IIIE+IV and V+VIW.
These rates of increase (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 (Hakamada and Matsuoka,
2014a). The point estimates from the other models range
from 1.1% to 4.4% for Area IIIE+IV and from −2.1% to0.6% for Area V+VIW, so that all lie within the 95% CI for
the abundance trend estimate for the model selected(Hakamada and Matsuoka, 2014a).

Figure 7.

The base case estimates of annual abundance together with their 95% CIs are compared to exponential trend estimated by the AICc-selected model i) of
equation (7) for Areas IV (upper panel) and V (lower
panel) (from Hakamada and Matsuoka, 2014a).

Abundance estimates averaged over survey modes
The inverse variance weighted averages of abundance estimates over survey modes for Areas IV and V are shownin Figure 7 with their 95% CIs. The point estimates of theweighted average abundance and their CVs (taking into account model error) are shown in Table 2 for Areas IV and V.
The CVs for these abundance estimates are all higher than33

Technical Reports of the Institute of Cetacean Research (2017)
Table 2
The abundance estimates for the base case scenario for Area IV (left) and Area V (right) (from Hakamada and Matsuoka, 2014a).
Year

PWA

CV(PWA)

Year

PWA

CV(PWA)

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

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

100,745
78,919104,013
99,680118,779
106,769
151,072
74,03067,661109,173

0.445
0.371
0.458
0.461
0.515
0.524
0.326
0.336
0.308
0.523

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 an averageof 23,984 (88%) for Area IV and 105,906 (109%) for Area
V (Figure 8). The estimates of annual rates of increase
and their 95% CIs change to 2.5% [−1.3%,6.3%] for Area
IV and −0.6% [−3.9%,2.6%] for Areas V, reﬂecting 1.5%
increase for the former and 1% decrease for the latter,and slightly less precision (an increase in standard errorof about 0.02) than when g(0) is assumed to be 1 becauseof the further variance introduced in estimating the g(0)values (Hakamada and Matsuoka, 2014a).
Figure 8.

Same plots as for Figure 7, but with the abundance
estimates and associated exponential model forthe base case replaced by the corresponding g(0)adjusted results. The IDCR/SOWER estimates for acommon northern boundary for CPII and CPIII asagreed by the 2012 IWC SC meeting are shown by
the yellow triangles (IWC, 2013); their conﬁdence
intervals include allowance for additional variance,
as do those for the JARPA and JARPAII surveys. The
dashed curves indicate the 95% CIs for the exponential model (from Hakamada and Matsuoka, 2014a).

those from a previous analysis (Hakamada et al., 2006)
because model error is now taken into account.
Abundance estimates and trends for the sensitivity tests
Besides the g(0) adjusted scenario, abundance estimatesfor Areas IV and V do not change substantially for thesensitivities excluding scenarios models other than themodel i) (i.e. the best model in the base case) was selected as the best model (Hakamada et al., 2013; Hakamada
and Matsuoka, 2014a). These annual abundance rate
34

DISCUSSION
Comparison of the abundance estimates from JARPA
and JARPAII with those based on IDCR/SOWER
As shown in Figure 8, abundance estimates for Antarctic
minke whales for Areas IV and V based on JARPA and
JARPAII agree with the estimates of Okamura and Kitakado (2012) based on IDCR/SOWER (Hakamada et al.,2013; Hakamada and Matsuoka, 2014a).
Application of JARPA and JARPAII abundance trends
As noted earlier, one of the features of JARPA and JARPA
II is that, unlike for the IDCR/SOWER program, surveys
have been repeated in the same area and in the samemonths every second year over a long period. Therefore,
the JARPA and JARPAII surveys facilitate both estimationof trends and the extent of inter-year variability in local
abundance. These abundance series as well as thosefrom IDCR/SOWER can be used to estimate abundancetrends using population dynamics models which incorporate catch-at-age data and so integrate information froma number of diﬀerent sources (Punt et al., 2014; MoriSighting survey procedures for abundance estimates of large whales in JARPA and JARPAII, and results for Antarctic minke whales

et al., 2006).
Through their use in such population models, theabundance estimates and trends derived from JARPA and
JARPAII which are reported in this paper provide information to complement that available to estimate sustainable
catch levels for minke whales in Areas IIIE, IV, V and VIW.
It should be noted that the abundance estimates inthis study were made on the basis of geographical Areas.
These estimates should be made on the basis of biological stocks deﬁnition in the future, as such information isalready available (Pastene and Goto, 2016).

ACKNOWLEDGEMENTS
This work would not have been possible without the considerable and appreciable eﬀorts of all the researchers,
captains, oﬃcers and crew members who have participated in the JARPA and JARPAII surveys.

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