SC/59/IA11

Improvements of the JARPA abundance estimation of
Antarctic minke whales based on JARPA Review
Meeting recommendations
Takashi Hakamada, Koji Matsuoka, Shigetoshi Nishiwaki
The Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055, Japan
Contact e-mail: hakamada@cetacean.jp
ABSTRACT
Several recommendations were offered during the JARPA Review Meeting (JRM) to improve theabundance estimations of Antarctic minke whale based on JARPA sighting data. The JRM established an
Advisory Group to facilitate the undertaking of these recommendations. This paper deals with fourrecommendations assigned as high priority by the Advisory Group: a) re-estimation of detection function
by pooling sighting data; b) investigation of ‘shoulder’ of detection function; c) extrapolation of density
to un-surveyed area, and d) abundance estimates accounting for the order that the strata were surveyed.
By pooling sighting data (i.e. increasing simple size) the shape of detection functions, including‘shoulder’, were improved in most of the cases (a and b above). By extrapolating density into
un-surveyed area, abundance estimates did not change substantially except in the case of stratum Prydz
Bay 2003/04 (c). Using linear model, reasonable estimate of date of peak migration could not be obtained
possibly due to the fact that JARPA sighting data in Areas IV and V are collected in short-term fromJanuary to February, in which abundance is rather stable because of the peak migration. Other
recommendations offered by the JRM will be considered in the near future.
KEY WORDS: ANTARCTIC MINKE WHALE, ABUNDANCE ESTIMATE, JARPA REVIEWMEETING.

INTRODUCTION
The abundances of the minke whales were estimated considering the recommendations at previous
IWC/SC meetings (Hakamada et al., 2006). At the JRM, an Advisory Group (Kitakado (convener),
Butterworth, Hedley, Matsuoka and Hakamada) was established to facilitate the undertaking of therecommendations and suggestions on abundance estimate given at the meeting listed in Table 1(IWC, 2007). The Group recognized that the immediate priority is getting the JARPA estimates tothe same level of acceptability as the current g(0)=1 IDCR estimates. Because there were many
recommendations, it was agreed that there was not enough time to cover all the recommendations
before IWC/SC meeting at Anchorage. As far as priorities before the SC meeting are concerned, theGroup should give more attention to addressing (at least initially) suggestions of possible bias, rather

1than focus on variance. Therefore, the Group identified high priority for following items.
1. Re-estimation of detection function by pooling (Re-estimate in the cases where the number ofdetection is small)
2. Investigate ‘shoulder’ of detection function (response to 3 in Table 1)
3. Extrapolation of density in unsurveyed area (response to 7 and 8 in Table 1)
4. Abundance estimates accounting for change in order that the strata were surveyed (response to 9
in Table 1).
This paper reports some results on work for these recommendations.
MATERIALS AND METHODS
Re-estimation of detection function
Recommendations 1 and 2 were dealt with in this section. In cases where the number of sightings isless than 15, data will be pooled over northern strata (combined strata of North-West andNorth-East)/ southern strata (combined strata of South-West, South-East and Prydz Bay for Area IV
and combined strata of South-West and South-East for Area V) as a part of task 1. In case that there
were less than 15 detections in northern strata, data were aggregated over whole of Area IV or V.
Estimated detection functions are shown in Figs 1-6.
For SSVs, the detection function has a clear shoulder in most of the cases. In discussion amongst by
the Advisory Group, it was suggested that the strata NE in 1991/92, NE in 1990/91, SE in 1992/93
and SE in 1996/97 don’t have clear shoulders, because the intercepts of the fitted curves with thevertical axis are highly influenced by the large number of sightings in the first 0.1 nm compared toneighbouring bins, with the fitted curves becoming flat only within this first bin. We comparedestimates of effective half search width in those strata with the average of same strata excludingthese estimates, respectively (Table 2).
For SV, though some of the detection function shows good fit but other detection functions don’t
have a clear shoulder, perhaps due to smaller samples size than in the case of SSVs. The Advisory
Group recognized that further investigation is necessary. In order to reconsider the detection
functions for SV, the Group suggested investigating followings.
1) Simply increase the maximum number criterion that avoids pooling, so that one pools in more cases the difficulty though this doesn't solve the problem where pooling has already occurred (and one wants if
possible to avoid pooling Northern and Southern strata, as weather conditions and hence effective searchhalf widths tend to differ between them). 2) In addition to 1), in cases where pooling doesn't solve thedifficulty, replace the w estimate for the N stratum (or similarly for S/PB), by the average value of w forthat stratum for all the cruises in that Area. 3). As 2) is rather ad hoc. a better (though it will mean morecomputations) way might be to analyse all the sightings for the N strata (similarly for S/PB) combined
across years using the DISTANCE option for the hazard rate function which estimates a common shapeparameter b for all the cruises, but a separate scale parameter a for each E/W stratum for each cruise.

2For this study, analysis 3) was conducted. To conduct 3), MCDS (Multiple Covariates Distance Sampling)
engine in DISTANCE ver. 5 was used. MCDS can incorporate covariates other than perpendiculardistance to estimate scale parameter of detection function. Smearing was not conducted because MCDS
does not support smearing. MCDS doesn’t conduct variable selection automatically therefore wecompared Akaike’s Information Criteria (AIC) for the models examined. Hazard rate function wasconsidered. Full model is described by
−b
⎧
⎞⎟ ⎫
f ( y ) = 1 − exp⎨− ⎛⎜ ya exp( EW + year ) ⎠ ⎬⎩ ⎝⎭where y is perpendicular distance and EW and year are covariates for east/west and year,
respectively.
Extrapolation to unsurveyed area
In this section, recommendation 3 was dealt with. Three types of extrapolation were considered:a) unsurveyed longitudinal sector due to insufficient time;b) stratum where coverage by the survey was small;c) gap between two strata.
Unsurveyed longitudinal sector due to insufficient time
The western part of SW stratum in Area IV was not surveyed in 1993/94, 2001/02 and 2003/04. The
western boundary of the stratum is 70oE longitudinal line and its eastern boundary is 100oE. The
longitudinal band between 70oE and 74oE was not surveyed in 1993/94 and that between 70oE and78oE was not surveyed in 2001/02 and 2003/04. Map of these unsurveyd area are shown in Fig 7.
For extrapolation, the density in the unsurveyed longitudinal sectors was estimated using data in
1989/90 and 1991/92 when whole of the stratum was covered. The data for 1995/96, 1997/98 and1999/2000 were not used for this extrapolation by the reason followings. In 1995/96, there was a gap
between SW and PB strata. In 1997/98, sea ice condition was so extraordinary that sighting vessels
could not survey in most part of PB. In 1999/2000, searching effort was insufficient due to bad
weather condition in the longitudinal sector between 70oE and 78oE. First, we estimate ratio of
density between 70-74oE to that between 74-100oE in 1989/90 and 1991/92 when whole of thestratum was surveyed. It is assumed that the estimated mean school size and effective half search
width are unchanged within the stratum therefore encounter rate was estimated instead of density.
Fig. 7 shows distribution of surveyed trackline by SSVs and unsurveyed area in 1993/94, 2001/02
and 2003/04. The ratio was estimated by

X y ,74 =Dy ,70 − 74
Dy , 74 −100(y=1989, 1991)(1)

Then the average of Xy, 74 over two years is estimated.

1
Xˆ 74 = (X 1989, 74 + X 1991, 74 )
2

(2)

3

Finally, we can estimate density in unsurveyed area for 1993/94 byDˆ 1993,70 − 74 = Xˆ 74 D1993, 74 −100

(3)

Similarly, we estimate density in unsurveyd area for 2001/02 and 2003/04 byDˆ 2001, 70 − 78 = Xˆ 78 D2001, 78 −100

(4)Dˆ 2003,70 − 78 = Xˆ 78 D2003, 78 −100

(5)

For the case of Eastern part of SW stratum in Area V in 2000/01, the method is similar to the case of
NW in 1993/94, 2001/02 and 2003/04. We used the data in 2002/03 when the configuration of iceedge is similar to that in 2000/01 to estimated extrapolated density.
Stratum where coverage by the survey was small
In 2003/04, SSVs surveyed PB stratum with low coverage. Map of this stratum is shown in Fig. 8.
For extrapolation, ratio of density Yy in PB to that in the SW stratum in year y was estimated forother years except 1997/98 when the PB stratum could not be surveyed due to sea ice.

Yy =Dy , PB
Dy , SW(y=1989, 1991, 1993, 1995, 1999, 2001)(6)

where Dy,PB and Dy,SW are density in stratum PB and SW in year y, respectively. For D1993,SW, D2001,SW,
and D2003,SW, the updated estimates described in previous section were used. Density in PB for
2003/04 was estimated by

D2003, PB = YˆD2003, SW(7)where Yˆ is average of Yy over the years indicated.
For the case of NW stratum in Area V for 2004/05, the method is similar to the case of PB for
2003/04. We estimated this average ratio of DNW/DNE for 1990/91, 1994/95 and 1996/97 andmultiplied the density in the NE stratum for 2004/05 by this ratio.
Gap between two strata
For a gap between PB and SW strata in 1995/96, the average density of two strata was applied. For asensitivity test, the density estimate in SW stratum was applied instead for the gap. The gap between
NW and SW for 1999/2000, 2001/02 and 2003/04 are also examples of c). Map of these gaps areillustrated in Fig. 9. In these gaps, the average density of two strata was again applied. For asensitivity test, the density estimate in NW stratum was used for the gap.
Abundance estimates accounting for change in order that the strata were surveyed
In order to deal with recommendation 9 in Table 1, a GLM is applied to estimate abundance accounting
for change in order that the strata were surveyed. We use a log linear model described by:4

log( Pobs ( y, a )) = log( Ptrue (0, a )) + αy + M + Q(T ) + ε(8)where y is year, a is stratum, Pobs(y,a) is observed abundance estimate, Ptrue(y,a) is unbiased abundance(i.e. free from survey mode and order effects) to be estimated in year y and in Area a. M is the mode
factor (distinguishing SV/SSV and closing/passing), T the covariate related to survey timing (i.e. middle
day of the survey period, order of the survey or month), Q(T) is a quadratic form of T with negative
coefficient for the term of second order with respect to T. ε the survey sampling error with varianceequal to [CVPobs(y,a)] 2.

RESULTSRe-estimation of detection functions
Table 2 shows comparison estimated esw’s and averaged ones among the other years. In the case of
NE for 1990/91, estimated esw and averaged one is different and this may suggest underestimate ofesw. But this may over correction because this substitution introduces an overall bias because one is
not also adjusting for potential biases for cases with the reverse features.
Table 3 shows AIC in each model for all cases. In most of the cases, esw was not different among themodel. This suggests that such examination would not affect on abundance estimate substantially.
Except one case, covariate of EW was not selected by AIC. Therefore it was suggested that covariate
EW would not affect estimate of esw significantly. Fig. 10 shows plots of detection functions foreach pooled data explained above. In case that the model including covariates of EW or year was
selected, the effects of these covariates were averaged. Comparing Figs 3-6, shape of detection
functions in this figure were improved.

Extrapolation of unsurveyed area
unsurveyed longitudinal sector due to insufficient time
As for Western part of SW stratum in Area IV in 1993/94, 2001/02 and 2003/04, Table 4 shows
correction factor estimated from data in 1989/90 and 1991/92. These results suggests density inunsurveyd longitudinal sectors (i.e. 70-74oE and 70-78oE) were higher than rest of SW stratum,
though it is not clear if it is significant. Extrapolated abundances in SW stratum were 4,285, 2,609
and 10,682 in 1993/94, 2001/02 and 2003/04, respectively. There estimates were higher thanprevious estimates in Hakamada et al. (2006).
As for Eastern part of SW stratum in Area V in 2000/01, Table 6 shows estimates of correction factor
and extrapolated abundance estimate in SW stratum for 2000/01. The abundance estimate is 7,059.
stratum where coverage by the survey was small

5

As for PB in 2003/04, Table 7 shows correction factors and estimated density in this stratum given
by formula (6) and (7). Estimated abundance is 26,467 whereas previous estimate in Hakamada et al.
(2006) was 41,273.
As for NW stratum in Area V for 2004/05, correction factor was obtained shown in Table 8. Using
this correction factor, extrapolated abundance is estimated as 15,780.
Gap between two strata
Table 9 shows comparison of abundance estimates in Area IV for 1995/96, 1999/2000, 2001/02 and2003/04 in case using average density in the two strata as a reference case and sensitivity test. The
difference in abundance estimate between two assumptions is small as shown in Table 9.
Abundance estimates accounting for change in order that the strata were surveyed
Table 10 shows estimated coefficients of the model in formula (8) applying data combined Areas IV
and V, Area IV and Area V, respectively. The first two cases, coefficient of T2 is positive contrary to
our expectation before the analyses though estimated coefficients with respect to T and T2 are notsignificantly different from 0. In the case of Area V, migration peak was estimated on 22 Feb. But
this doesn’t agree with previous study (Kasamatsu et al., 1996). It is suggested that model fails toestimate data of migration peak.
DISCUSSIONS
Re-estimation of detection function
Pooling data improve the shape of detection functions for most of the cases. But some of thefunctions (e.g. NE in 1990/91 for SSVs) still need further investigation on the shape of the function.
For example, applying other key function (e.g. Half-normal function) and Kelker strip approach average the first two bins. AIC selected covariate year for some cases whereas covariate EW was notselected except one case. But whether or not considering covariate of year, estimate of esw are notsubstantially different as shown in Table 3. Therefore, it is suggested that considering thesecovariates could not differ abundance estimate substantially even if the covariates are selected by
AIC.
Extrapolation of unsurveyed areas or gaps between two strata.
Extrapolated abundance estimates don’t differ substantially from previous estimates in Hakamada etal. (2006) except the case of PB in 2003/04 and NW in 2004/05. It is suggested that different methodof extrapolation could not affect on abundance estimate significantly.
Abundance considering the migration peakForm the estimated coefficients, estimated coefficients with respect to T and T2 are not significantly
different from 0 for the case of the combined Areas and the case of Area IV. Estimated migration

6

peak is not plausible in Area V because this result doesn’t agree with previous study (Ohsumi, 1979;
Kato and Miyashita, 1991; Kasamatsu et al., 1996; Miyashita et al, 2001; Minamikawa et al., 2003).
One possible reason is that most of sighting data in Areas IV and V used in this analysis wereobtained in short-term in which abundance was stable as the peak of the migration (January andFebruary). Fig 11 shows plot of mid-day of the survey period and logarithm of abundance estimate
for each stratum in Areas IV and V, respectively. From the figure, no tendency of peak migration isobserved. Further investigation of formula of the model and definition of T could improve theperformance of the model. By improving formula (8), additional variance can be estimated which iscorrespond to recommendation 10 in Table 1. This improved model should be investigated in near
future.
ACKNOWLEDGEMENT
We thank all researchers and crews involved JARPA surveys. We thank the members of Advisory
Group (Drs. Doug Butterworth, Sharon Hedley and Toshihide Kitakado) for their helpful suggestions
and comments on methodologies and manuscript. We also thank Drs Hiroshi Hatanaka (ICR) and
Hidehiro Kato (Tokyo University of Marine Science and Technology) for their useful comments toimprove manuscript. We thank Mr. Hiroshi Kiwada and Ms. Tomoko Hasegawa (ICR) for theirassistance.
REFERENCES
Hakamada, T., Matsuoka, K. and Nishiwaki, S. 2006. An update of Antarctic minke whales
abundance estimate based on JARPA data. Paper SC/D06/J06 presented to Intersessional Workshop
to review data and results from special permit research on Minke Whales in the Antarctic December
2006 (unpublished). 36pp.
International Whaling Commission. 2007. Report of the Intersessional Workshop to review data andresults from special permit research on Minke Whales in the Antarctic, Tokyo 4-8 December 2006.
Paper SC/59/Rep1 presented to IWC Scientific Committee May 2007. 48pp.
Kato, H. and Miyashita, T. 1991. Migration strategy of southern minke whales in relation to
reproductive cycles estimated from foetal length. Rep. int. Whal. Commn. 41: 363-9.
Kasamatsu, F., Joyce, G. G., Ensor, P. and Mermoz, J. 1996. Current occurrence of baleen whales in
the Antarctic waters. Rep. int. Whal. Commn. 46: 293-304.
Minamikawa, S., Miyashita, T. and Okamura, H. 2003. Further analysis on the JSV data of the
Antarctic minke whale using the GLMs. Paper SC/54/IA12 presented to IWC Scientific Committee,
May, 2002. (unpublished) 11pp.
Miyashita, T., Shono, H and Okamura, H. 2001. GLM analysis of the JSV data for the Antarctic
minke whale. Paper SC/53/IA11 presented to IWC Scientific Committee, June, 2001. (unpublished)
6pp.
Ohsumi, S. 1979. Population assessment of the Antarctic minke whale. Rep. int. Whal. Commn. 29:
407-20.

7

Table 1. List of the tasks recommended at the workshop and suggested task (3 and 4) and their priority in
the Advisory Group.

tasks

Priority

1. Estimation of detection function（Re-estimate

Hin the cases where the number of detection issmall ）
2. Investigation of sensitivities to pooling allMvessels to estimate effective strip width andmean school size.
3. Investigate ‘shoulder’ of detection function

H

4. Sensitivity analysis without smearing

M

5. Variance estimation from the SSV dataM

6.

appropriate

M

7. Abundance estimates treating as if abundanceL

Sensitivity

analysiswithweighting and/or bootstrappingin gaps between two strata were 0.
8. Extrapolation of density in unsurveyed areaH

9. Abundance estimates accounting for change inHorder that the strata were surveyed
10. Estimation of additional varianceM

11. Revised annual increasing rate and its CV

Mfollowing suggestions1-8

8

Table 2. Comparison of effective half search width (esw) in each stratum with average among otheryears in the case that the detection function didn’t has a clear shoulder. Pold represents abundance
estimate in Hakamada et al. (2006). Pnew represents abundance estimate substituting averaged esw
instead of estimated one in the stratum.
stratum

Estimate (CV)average

Pold

Pnew/Pold

1991/92 NE in Area IV

0.504 (0.566)

0.672

2,828

0.750

1990/91 NE in Area V

0.260 (0.477)

0.639

26,152

0.407

1992/93 SE in Area V

0.668 (0.225)

0.729

25,7950.916

1996/97 SE in Area V

0.596 (0.207)

0.729

63,532

0.818

Table 3. AIC and estimated esw for each model explained in the text. Bold letter indicates selectedmodel by AIC.
IV N in closing mode
covariates AIC
none
701.3
EW
727.4
734.6
year
EW+year736.6
IV S in closing mode
covariates AIC
none
2251.8EW
2294.42259.2
year
2260.2
EW+year

esw
0.332
0.631
0.419
0.628

esw
0.522
0.824
0.512
0.486

CV
0.150
0.061
0.063
0.063

IV N in passing mode
covariates AIC
none
573.2
EW
574.0
year
570.0
EW+year
572.1

esw
0.397
0.347
0.401
0.431

CV
0.173
0.091
0.086
0.084

CV
0.082
0.031
0.040
0.041

IV S in passing mode
covariates AIC
none
2622.5EW
2604.1year
2614.9EW+year2597.0

esw
0.803
0.746
0.733
0.708

CV
0.041
0.032
0.032
0.034

esw
0.675
0.660

CV
0.117
0.049

PB in closing mode
covariates AIC
none
967.0
year
965.4

esw
0.662
0.539

CV
0.120
0.074

PB in passing mode
covariates AIC
none
1362.31363.3year

V N in closing mode
covariates AIC
none
1159.7EW
1164.21168.8year
1179.2
EW+year

esw
0.416
0.437
0.393
0.423

CV
0.110
0.056
0.060
0.065

V N in passing mode
covariates AIC
none
1182.4EW
1191.9year
1186.7
EW+year1189.1

esw
0.473
0.680
0.451
0.441

CV
0.142
0.048
0.060
0.062

V S in closing mode
covariates AIC
none
4370.0EW
4378.9year
4366.34369.4
EW+year

esw
0.604
0.627
0.521
0.530

CV
0.069
0.026
0.032
0.031

V S in passing mode
covariates AIC
none
6159.4EW
6162.2
year6154.5
EW+year6156.2

esw
0.609
0.599
0.601
0.583

CV
0.059
0.022
0.023
0.023

9

Table 3. Coefficient of detection functions selected by AIC shown in Fig 10.
Area IV
N in closing mode
S in closing mode
PB in closing mode
N in passing mode
S in passing mode
PB in passing modease (a )
0.160 0.042
0.281 0.043
0.265 0.190
2.327 10.760
0.792 1.263
0.376 0.093bse (b ) EW se (EW ) y (91/92) se y (93/94) se y (95/96) se y (99/00) se y (01/02) se y (03/04) se
1.454 0.195
1.440 0.151
1.539 1.694
0.764 0.979 -0.766 1.016 -0.374 1.028
0.109 1.097
0.701 1.006
0.455 1.131
1.462 2.878-2.357 3.952 -2.740 3.963 -2.083 3.957
2.378 1.478 -0.411
0.116
0.128 0.383 -0.363 0.371 -0.090 0.394
1.262 0.235

Area VN in closing mode
S in closing mode
N in passing mode
S in passing modease (a ) bse (b ) EW
0.217 0.042 1.510 0.178
0.310 0.089 1.000 0.751
0.213 0.062 1.184 0.175
0.365 0.063 1.098 0.560

se (EW ) y (92/93) se-0.677 0.376

y (94/95) se
0.067 0.381

y (98/99) sey (00/01) sey (02/03) se-1.606 0.986-1.474 0.729-0.114 0.554-0.549 0.323

0.162 0.273-0.396 0.203* b estimate of 1.000 in S in passing mode for Area V is lower bound.
Table 4. Estimate of extrapolation factor in unserveyed longitude band in SW stratum in Area IV.
statisitics

estimate

statisitics

estimate

X74,1989

1.913

X78,1989

0.876

X74,1991

1.438

X78,1991

2.840

X̂ 74

1.675

X̂ 78

1.935

Table 5. Comparison between previous estimates in Hakamada et al (2006) and the extrapolated
estimates using extrapolation factors in Table 4.

1993/94 2001/02 2003/04
previous
4,000
2,087
8,780
extrapolated
4,285
2,609
10,682

Table 6. Estimate of extrapolation factor in unserveyed longitude band in SW stratum in Area V
using data in 2002/03.
statistics

estimate

Estimated correction factor

1.140

Previous abundance estimate

6,613

Extrapolated abundance

7,059

10

y (04/05) se-0.039 0.494

Table 7. Ratio of whale density in SW to that in PB for years examined and estimated one in PB in
2003/04.

1989/90
1991/92
1993/94
1995/96
1999/00
2001/02
average
2003/04

SW
0.071
0.073
0.106
0.097
0.241
0.078

PB
0.062
0.331
0.172
0.086
0.257
0.730

0.278

0.702

DSW/DPB
0.874
4.557
1.615
0.891
1.068
9.388
3.066

Table 8. Ratio of whale density in NW to that in NE for years examined and estimated one in NW
in 2004/05.

NE
1990/91
1994/95
1996/97
average
2004/05

NW
0.076
0.060
0.052
0.055

DNE/DNW
0.128
1.699
0.042
0.695
0.036
0.684
1.026
0.057

Table 9. Comparison of abundance estimate in Area IV for between reference case (i.e. using
average density of

1995/96
1999/00
2001/02
2003/04

two strata) and sensitivity test.

reference sensitibity
28,67828,91945,54942,85247,26846,87755,363
46,97511

Table 10. Estimated coefficients of the model in formula (8) applied to combined Areas IV and V,
Area IV and Area V, respectively.
Combined Areas IV and V

factor(S)41
factor(S)42
factor(S)43
factor(S)44
factor(S)45
factor(S)51
factor(S)52
factor(S)53
factor(S)54
factor(M)2factor(M)3y
I(T)
I(T^2)

Estimate Std. Error t value
Pr(>|t|)
7.681
0.704
10.912 <2e-16
7.909
0.705
11.223 <2e-16
8.441
0.660
12.793 <2e-16
8.602
0.716
12.024 <2e-16
8.256
0.711
11.606 <2e-16
8.793
0.665
13.216 <2e-16
9.890
0.735
13.46 <2e-16
9.389
0.732
12.827 <2e-16
9.834
0.694
14.175 <2e-16-0.1130.218-0.5170.606-0.2700.176-1.5360.127-0.002
0.012
-0.16
0.873
0.00754 0.02129
0.354
0.724
0.00005 0.00017
0.272
0.786

Area IV

factor(S)41
factor(S)42
factor(S)43
factor(S)44
factor(S)45
factor(M)2factor(M)3y
I(T)
I(T^2)

Estimate Std. Error t value
Pr(>|t|)
9.257
0.979
9.454 9.03E-15
9.511
0.987
9.633 3.97E-15
9.906
0.928
10.679 < 2e-16
10.2271.009
10.136 4.03E-16
9.763
0.970
10.065 5.56E-16-0.1940.301-0.6470.5196-0.2810.249-1.1290.26230.00040.017
0.022
0.9827-0.04707 0.03079-1.5290.13020.00048 0.00025
1.926
0.0576

Area Vfactor(S)51
factor(S)52
factor(S)53
factor(S)54
factor(M)2factor(M)3y
I(T)
I(T^2)

Estimate Std. Error t value
Pr(>|t|)
6.949
0.992
7.005 1.59E-09
7.851
1.086
7.226 6.41E-10
7.328
1.080
6.785 3.90E-09
7.975
0.999
7.979 2.89E-11
0.031
0.311
0.1
0.9203-0.2710.243-1.1140.2691-0.0110.016-0.6710.50430.07155 0.03132
2.284
0.0256-0.00042 0.00023-1.8060.075512

Fig. 1. Detection functions for each stratum in Area IV by SSVs. n is the number of sightings withinstratum (before smearing) used for estimation of detection function.

13

Fig. 2. Detection functions for each stratum in Area V by SSVs. n is the number of sightings withinstratum (before smearing) used for estimation of detection function.

14

Fig. 3. Detection functions for each stratum in Area IV by SV in closing mode. n is the number ofsightings within stratum (before smearing) used for estimation of detection function.

15

Fig. 4. Detection functions for each stratum in Area V by SV in closing mode. n is the number ofsightings within stratum (before smearing) used for estimation of detection function.

16

Fig. 5. Detection functions for each stratum in Area IV by SV in passing mode. n is the number ofsightings within stratum (before smearing) used for estimation of detection function.

17

Fig. 6. Detection functions for each stratum in Area V by SV in passing mode. n is the number ofsightings within stratum (before smearing) used for estimation of detection function.

18

1989/90

1991/92

74oE
UA

1993/94
Fig. 7. Distribution of sighting effort in western part of Area IV (70-100oE) south of 60oS for
1989/90, 1991/92, 1993/94, 2001/02 and 2003/04 and unsurveyed area (UA) in SW stratum from the
top for 1993/94, 2001/02 and 2003/04, respectively.

19

UA

78oE

2001/02

78oE
UA

2003/04
Fig 7 (continued).

NW

GAP
SW
PB

Fig. 8.Coverage in Prydz Bay (south of 66oS) was low in 2003/04. NW, SW, PB and GAP represents
North-West, South-West, Prydz Bay strata and the gap respectively.

20

NW
SW
GAP
PB

1995/96

NW

GAP

GAP
SW
PB

SW

1999/2000

NW

GAP
SW
PB

2001/02
Fig 9. Gaps between two strata in western part of Area IV for 1995/96, 1999/2000, 2001/02 and2003/04.

21

NW
GAP
SW
PB

2003/04
Fig. 9. (Continued.)22

closing
1

passing

n =155n =118n =451n =526n =184n =261n =244n =236n =848n =1202

N in
Area IV

01

S in
Area IVPB in
Area IV

N in
Area V

Detection probability

0

1

0

1

0

1

S in
Area V

0
0

1.5 0

1.5

Perpendicular distanceFig. 10. Detection function pooled data over years for SV incorporating covariates.

23

Area IV
12

10

log(P)

8

6

4

SW
SE
NW
NE
PB

2

0
0

20

40
60
mid-day of the survey period

100

80

Area V1412

10

log(P)

8

6
SW
SE
NW
NE

4

2

0
0

20

40
60
80
mid-day of the survey period

100

120

Fig. 11. Plot of mid-day of the survey period and log of abundance estimate for each stratum.

24