SC/F16/JR27

Analyses of body condition in sei, Bryde’s and common
minke whales in the western North Pacific with JARPN
and JARPN II dataset.
Kenji Konishi

Institute of Cetacean Research, 4-5, Toyomi-cho, Cho-ku, Tokyo, 104-0055, Japan
Contact e-mail:konishi@cetacean.jp
ABSTRACTThe annual trend in energy storage in sei Balaenoptera borealis and Bryde’s B. edeni during the JARPN II period and common
minke whales B. acutorostrata during the JARPN and JARPN II period were examined. Regression analyses showed that blubber
thickness and half girth in sei whales have been increasing during 2002-2015. The increase per year is estimated at 0.109 ±0.038
SE cm for mid-lateral blubber thickness and 2.183 ±1.379 SE cm for axillary half-girth. The regression analyses also showednegative year effects on blubber thickness in common minke whale while Bryde’s whale, year effects were not seen. The regression
analyses also showed that sei and Bryde’s whale have good body condition with larger body length and in later days. Some analyses
also showed body condition and its seasonal increase differ among maturity stages. Meanwhile, prey species and surface watertemperature where whale were caught were not selected as predictors. The reasons for increase body condition in sei and decrease
body condition in minke whale are difficult to identify. However, increase trend of body condition in sei whales suggest thefavourable food availability change for sei whale in the study area. The regression also showed decrease body condition in common
minke whale using JARPN and JARPNII (both Offshore and Coastal components) suggesting unfavourable food availability change
and difference between stocks, however the analysis for the minke whale leave to be improved.

KEYWORDS: MINKE WHALES, ENERGY STORAGE, ECOSYSTEM CHANGES, JARPN II, NORTH PACIFICINTRODUCTION
Marine mammals, such as whale and pinniped, stock energy by food intake into its blubber as lipids, and thisincrease and decrease in lipid content changes its thickness and mass (Lockyer, 1987; Víkingsson, 1995; Næss etal., 1998; Konishi, 2006; Field et al., 2007; Williams et al., 2007; Konishi et al., 2008; Christensen et al., 2013;
Konishi and Walløe, in press). Therefore, the measurement of blubber thickness and girth have been used asbody condition indices that can be used to monitor history of food intake, such as increase through the feeding
season, local food availability and long-term change in relation to environmental change. Because precise
measurements of blubber thickness, girth or fat weight need lethal sampling, these informative results have beenreported and discussed in IWC-SC (IWC, 2015; Konishi et al., 2008; Christensen et al., 2013; Konishi andWalløe, in press). In previous IWC-SC, “the Working Group to Address Multi-Species and Ecosystem Modelling
Approaches” also considered that indices of body condition are potentially of importance to ecosystemmodelling, because they can enable detection of changes over a shorter time period than changes in abundance(IWC, 2012). Furthermore in the long-term continuous monitoring of food availability as indicated by stomach
contents and of energy storage in the form of blubber thickness can contribute important information for the
management and conservation under the mandates of both the IWC and CCAMLR of the krill ﬁshery and of the
predators that depend on krill for food in the Southern Ocean (Konishi et al., 2014). These previous studies anddiscussion in the Scientific Committee also motivated the adaptation of the examination of body condition trend
in the North Pacific baleen whales off the Pacific coast of Japan.
This study is listed in the section “Objective 1 Feeding ecology and ecosystem studies Sub-objective 7” of
JARPN II framework paper (Tamura et al., 2016: SC/F16/JR1). Although the body condition analyses was notincluded in the original JARPN II survey plan, this study is related to the understanding of the mechanism of
feeding ecology and the role of a component of the ecosystem. Waters off the pacific coast of Japan have richfishery ground where the Kuroshio and Oyashio Currents meet, and the current direction goes offshore carrying
pelagic fish which is important prey for baleen whales (Konishi et al., 2009). JARPN and JARPNII surveys haveexamined the feeding ecology of sei Balaenoptera borealis, Bryde’s B.edeni and minke whale B. acutorostrata
with food and oceanographic environment occasionally, therefore linkage of this information to the nutritional
study are useful for understanding the ecosystem study.

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SC/F16/JR27

The purpose of this study is to examine the time trends of blubber thickness and what factors influence theenergy storage of sei and Bryde’s, and common minke whales in the western North Pacific. This study also aims
to integrate a study using feeding ecology and biological data from stomach contents and measurements andgeographical difference and oceanography from using sighting info data near sea surface temperature. The longterm dataset also includes dynamics of those data that will contribute to the ecosystem study. To conduct
comprehensive analyses, data from JARPN and JARPN II including both offshore and coastal components areused for analyses in common minke whale.

METHODS
Survey area and Sampling
The research area of JARPN and JARPN II was the area between the Pacific coast of Japan and 170°E, and alatitudinal range between 35º N and 50° N (Figure 1), including a part of sub-areas 7, 8 and 9 established by the
Scientific Committee of the International Whaling Commission (IWC, 1994). Whales used in this study were
sampled during 2002–2015 for sei whales, during 2000-2013 for Bryde’s whales from offshore component andduring 1994-2015 from common minke whales from both offshore component and coastal component. Sampling
was conducted from May to October in offshore component, April-May in coastal component in spring and
September-November in coastal component in fall. The whales were sampled by sighting and sampling vessels(SSV) at offshore component and positions of the sampled whales found and sea surface temperature (SST) werealso recorded. Then the whales were transported to a base research vessel (Nisshin-Maru) where they were
examined by biologists. In coastal component, whales sampled by SSV were dissected at a land base station at
Sanriku and Kushiro regions. All whales were measured, weighed, and their reproductive organs were examined
to distinguish sexual maturity. Males of sei, Bryde's, and common minke whales, were defined as sexually matureby testis weights (larger side) of more than 1090 g, 560 g and 290 g, respectively (Bando et al., pers. comm.,
Institute of Cetacean Research, Toyomi 4-5, Chuo-ku, Tokyo 104-0055, Japan). Females were defined as sexuallymature by the occurrence of at least one corpus luteum or albicans in their ovaries and the presence of foetus andmammary gland was observed to examine lactation. The category of sexual maturity and the number of sample
sizes used in this study are listed in Table 1, 2, and 3.
Blubber thickness was measured to the nearest mm, by dissecting perpendicularly from skin to muscle without
including connective tissue or black surface skin at the level of dorsal fin and umbilicus. Half girth was alsomeasured at the level of axillary, umbilicus and anus. The measurement positions are shown in Figure 2. The
lateral blubber for blubber thickness measurements has uniformity in thickness and is easy for precise measurement.
Any tension in the blubber tissue was released by cutting through the blubber layer down to the muscle fascia
around the measurement site. Fat weight of all the blubber including the ventral groove and visceral fat had been
weighed in the first animal caught each day and one of the fine proxy for body condition (see Konishi et al., 2008;
Konishi and Walløe, in press), however, fat weight was not used in this study because of the limited measurements
for fat weight after 2010.
To examine the feeding activity when a whale was caught and its body condition, stomach content data including
the main prey item in the forestomach was used. (see detail of stomach content treatment in Tamura et al., 2016:
SC/F16/JR15). Stock structure of common minke whale in the Western North Pacific can be divided into two subpopulation “J and O” by genetic definition. We also included the stock information at 75% confidential level(Pastene et al. JR38). Since the three baleen whales distribute through the different oceanographic waters anddifferent feeding histories (see also Konishi et al., 2016: SC/F16/JR23), water temperature near surface “wtemp”
was included as potential covariate.

Statistical analyses
All response (body condition indicators) and predictor variables are listed in Table 4. To make a biologicallyplausible full model, independent variables including continuous and categorical variables are included at first,
then quadratic terms of continuous variables are also added to see any non-linear effects. If quadratic term is muchbetter than non-quadratic term based on the p-values, the quadratic term was used in the full model. To consider
potential interaction effects in the model fitting, variables of interaction term were selected based on thedendrogram of basic models. The dendrogram was illustrated using package “tree” 1.0 in R (Ripley, 2015), andselected variables which were likely to give strong effects on responsible variable were used to consist ofinteraction terms. The regression with a model with all two-way interaction terms were analysed and the interaction
terms far from p-value (>= 0.1) were deleted. Then three-way interaction terms were composed by the combination

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of remaining two-way interaction variables and were added into the model (Crawley, 2005). We used thisbiologically plausible model with potential interaction terms for the step-wise procedure with 1) random effects toaccount for the sampling heterogeneity and 2) selection of fixed effects.
A statistical model is needed to account for the effects of date and location when making inferences about possible
year trends (de la Mare, 2011). To account for the effects, we included random-effects terms in a fixed-effects
model. One of the random effects is intercept of categorical year “YearCat” assuming that random slope andintercept in other variables among years. For fitting models that estimate the growth in blubber to be different inspace and time, random effects of date “DateNum” divided by “LatCat” and “LongCat” were also tested if
inclusion of these random effects bring better fit. These random effects accounted for samples from different dates
at different places throughout the survey period by a single operation as used in JARPN and JARPN II cruise.

The step-wise procedure used in this study basically followed one used in Konishi and Walløe (in press) whichwere also recommended by the JARPA II review panel (IWC, 2014) for the study of body condition trend in
Antarctic minke whale. To evaluate fitting of the models, the Bayesian information criterion (BIC) (Schwarz,1978) was used. The use of Maximum Likelihood (ML) or Restricted Maximum Likelihood (REML) can be
explained as follows (see also Zuur et al., 2009).
(1) The first step is to decide which random effects to include. The models should then be fitted using REML.
(2) The next step is to systematically try to eliminate some of the fixed effects. The models should then be
fitted using maximum likelihood but order of elimination is started from three-way interaction term, two-way
interaction term then other fixed-effects terms.
(3)

When the best model has been identified in step (2), this model should be fitted using REML.

All statistical analyses were conducted in R environment version 3.2.2 (R Core Team, 2015) using package “lme4”
version 1.110 (Bates et al., 2015) for mixed effects models, “LMERConvenienceFunctions” version 2.10
(Tremblay, 2015) for the above stepwise model selection (2) and (3) (Tremblay, 2011) was used. Packages “sjPlot”(Ludecke, 2015) and “ggplot2” (Wickham, 2009) were used to visualise modelling results.
RESULTSSei whale
Full models and selected best models with BT11, BT7, umbilicus Girth and axillary girth as response variables forsei whale were listed in Table 5, and dendrogram to select component of interaction term for each response variable
were drawn in Figure 3, 4, 5, and 6. In the regression analyses for blubber thickness at the mid-lateral point below
the dorsal fin (BT11) in sei whale, the best model selected by BIC included YearNum, BLm2, DateNum,
PreMaturity_Cat, Interaction terms of DateNum:BLm, YearNum:DateNum and DateNum:PreMaturity_Cat at the
5 % level and FirstS (t=-1.715) and wtemp(t=1.242) (Table 6) with paired correlation plots and diagnostic plots(see Appendix 1). The year effect indicates that the blubber thickness at the level of dorsal fin (BT11) significantly
increased during 13 years (2002-2015) by 0.109 ± 0.038SE (cm/year). In other predictive effects, whales of large
body length and later date have positive effects on BT11 at 5% level. BT11 also significantly differ among maturity
stage, for example mature females have thicker blubber and pregnant and lactating females and mature males havethinner blubber than immature females. The main prey species, categorical term of latitude and longitude were notincluded in the best model, suggesting these variables do not have effects on the change of BT11. The threeinteraction terms were also included in the best model for BT11 and illustrated as representative of interaction
terms. The year effects depend on the value of date (Figure 7). BT11 also significantly increased with BLm and
DateNum while the effects of DateNum depend on the effects of BLm (Figure 8) and maturity stage(PreMaturity_Cat; Figure 9). There were significant difference of BT11 among maturity stage (PreMaturity_Cat).
The best model for BT11 also included random intercept of year as categorical (1|YearCat).
In the regression analyses for blubber thickness at the mid-lateral point at the level of umbilicus (BT7) in sei
whale(Table 7), the best model selected by BIC included BLm, DateNum, latitude, weight of first stomach andmaturity stage with an interaction of body length and latitude. Random intercept of year (1|YearCat), body lengthand date were included as well as in BT11. However “YearNum” was not selected in the best model. Diagnostic
plots for the best model are shown in Appendix 1.
In the best models with umbilicus and axillary girth, body length, date and maturity stage in the same manner as
blubber thickness (Table 8 and 9). The weight of forestomach content was only significant in umbilicus girth but
not in axillary half-girth, while significant increase of girth with year (2.183 ± 1.379SE cm) was found in axillaryhalf-girth. Diagnostic plots for the best model are shown in Appendix 1.

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SC/F16/JR27

In the analyses using BT7 and umbilicus girth, the both best models did not include year effect while negative andpositive effect of stomach content weight “FirstS” were included at 5% level, respectively.
Bryde’s whale

Full models and selected best models with BT11, BT7, umbilicus Girth and axillary girth as response
variables for Bryde’s whale were listed in Table 10. Dendrograms to select component of interaction term
for each response variable were drawn in Figure 10, 11, 12, and 13. In the regression analyses for Bryde’s
whale, the best models selected by BIC included BLm, DateNum and PreMaturity_Cat at the 5 % level(Table 11, 12, 13 and 14), showing larger animals in later days are thicker in blubber thickness and wider ingirth. No interaction terms were selected in any best models without year and body length in axillary girth,suggesting year effect differs in relation to body length. Significant year effect was only found in response
variable of axillary half-girth, showing increase of the coefficient 3.848 ± 1.824SE cm per year during
2000-2013. Random intercept of categorical year “YearCat” were included into all four best models for
Bryde’s whale. Other diagnostic plots for the best model are shown in Appendix 1.

Common minke whale
Common minke whales were sampled from JARPA period and followed by JARPNII offshore and in two coastal
regions. To examine the long-term trend since 1994, data with consistency throughout the years were used for the
analyses. Therefore, some independent variables, such as water temperature “wtemp” and feeding information
were not included in a full model. Stock structure of common minke whales in the Western North Pacific can bedivided into two sub-populations: “J and O” by genetic definition. This stock information (Pastene et al., 2016:
SC/F16JR38) was included as one of the independent variables in a full model.
Full models and selected best models with BT11, BT7, umbilicus Girth and axillary girth as response variables forcommon minke whale were listed in Table 15. Dendrogram to select component of interaction term for each
response variable were drawn in Figure 14, 15, 16, and 17. In the regression analyses for common whale using
BT11, the number of fixed effects are selected more than other whales and the best models selected by BIC
included BLm, DateNum, F_lat, F_long, Maturity state (PreMaturity_Cat), LatCat, stock information (OJ75) andthree interaction terms(Table 16, 17, 18 and 19). These terms are also significant at the 5 % level. Negative year
effect was only included and significant at 5 % level in BT11 -0.051 ±0.015 cm per year. Year effect was included
using BT7 but was not significant (t = -1.640). The date and body effects were included in all best models, however
the coefficients totally differ among body condition indices including negative and positive values, suggesting nocommon effects were found for these variables. Other diagnostic plots for the best model are shown in Appendix
1.
To conduct integrated analyses from JARPN II survey, we included prey information from the stomach content
analyses taken in JARPN II for sei and Bryde’s whales, however main prey species (MainPreyS) were not selected
throughout the analyses and water temperature was not included or not significant at most of best models.

DISCUSSION
Sei whale
In the regression analyses for sei whales, the best models using BT11 and axillary girth show that the positive year
effects at 5% level, indicating that food environment for sei whale has been becoming favourable during JARPN
II period. In the regression analyses using other two response variable, that is BT7 and umbilicus half-girth, year
was not included in the best model, suggesting the sensitivity of the body condition for energy intake differ among
blubber thickness and girth measurement position and these positions are less sensitive than BT11. Blubber
thicknesses at BT7 and umbilicus birth are both near internal organs and these measurement areas could possibly
be affected by other factor such as stomach fullness and pregnancy. BT11 is a more posterior area from umbilicus
level and likely to be a good indicator. Positive effects of body length and date on body condition are biologicallyreasonable and similar results were reported in the Antarctic minke whale and common minke whale in Atlantic
waters (Konishi et al., 2008; Konishi and Walløe, in press; Christensen et al., 2013).

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SC/F16/JR27

In the analyses using BT11, quadratic body length (BLm2) was selected and its coefficient is positive value,indicating small animals tend to have thicker blubber in the early period of lifetime when they are thinner andagain gain thicker blubber in relation to their growth. This may suggest fatting in the period of lactating remainedin the small animals. The inclusion of interaction terms also demonstrated that change in blubber thickness
accordance with date change in relation to growth and year. The regression analyses also showed the difference ofincrease in blubber thickness in accordance with date among maturity stage. Interestingly only mature females
have negative correlation with date while other maturity stages showed increase with days. The exact interpretation
for this needs further behavioural difference among maturity stages, especially non-pregnant mature females.
From the analyses using BT7 and umbilicus girth, negative and positive effects of stomach content were detected,
respectively. This possibly suggests that these two measurement points related to umbilicus position are somewhat
less sensitive than other anterior and posterior levels of the body, such as BT11 and axillary position in sei whales.
Sei whales sometimes feed and stock full of pelagic fish in the forestomach exceeding 500kg although the amountof copepod or euphausiids prey is usually decent in JARPN II. The effects of food intake on the stomach need to
be studied further, however the interpretation from the results using BT11 and axillary girth will likely be more
precise when we see the yearly trend as we mentioned above.
Bryde’s whale
In the regression analyses for Bryde’s whale, Year effect was not included in the best model without a case foraxillary half-girth. In the best model using axillary half-girth, the coefficient of body length is a distant value. Thislarge coefficient was caused by inclusion of interaction of YearNum and BLm. When we exclude this interaction,
the coefficient of BLm becomes 20.000 ± 0.853SE cm while year effect becomes negative -5.901 ± 1.130SE cm.
Therefore we could not conclude the results for axillary girth so far. Eventually we did not find any trends ofenergy storage during 2002-2013 period in Bryde’s whale in the survey area.
In all analyses, positive effects of body length and date on body condition are included and significant at 5% level,
suggesting the model could capture the increase of energy deposit during feeding season and change in body length.
Maturity state is also an important effect on body condition and pregnant females have better body condition than
immature females.

Common minke whale
Data source from common minke whales has different situation compared to other two whale species. In the resultsfrom analyses for common minke whales, we tried to conduct analyses using integrated datasets including previous
JARPN data and two different components in JARPN II. Among the analyses with these data pooled, we did not
find consistent trends among four body condition indices. In this study, we used data from both JARPN since 2003
in Sanriku, 1994 to 1999 and JARPN II since 2000 to 2013 including coastal components since 2002. In the future
study, stratified analyses for each dataset sharing common features are needed to catch exact yearly trends andbiological features in addition to testing different types of independent variables. From the regression analyses,
minke whales have thicker blubber at higher latitude and in western regions, in “O-stock”, and are different
between latitude stratum at BT11.

For the purpose of integrating analyses using stomach content and oceanography, we included main prey species
and surface water temperature into a full model. Unfortunately, these variables were not included or were included
but not statistically significant in the best models for all whale species and body condition indices without
exception. In the trend analyses, this study showed that sei whales substantially increase in blubber thickness andhalf girth. Although this interpretation is difficult from only our results, the feeding habits have been drasticallychanged with decreasing the occurrence of Japanese anchovy since early 2000s (Konishi et al., 2016:
SC/F16/JR23). The change of feeding habits was not seen in Bryde’s whales because of high dependency on
euphausiids (Konishi et al., 2016: SC/F16/JR23). The results of this study did not show the relationship between
prey species as snap shot and body condition, however the amount of food intake and difference of prey species,
such as calorie and feeding efficiency, are important research for future energetic study. The body condition
analysis also needs to be developed for management purposes.

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ACKNOWLEDGEMENTS
We would like to thank all the captains and crews of the ships that took part and the scientists who were involved
in JARPN and JARPN II surveys. We would also like to thank Japanese colleagues, and other people who havehelped with and made valuable comments on this paper. The JARPN and JARPN II programs were conductedwith permission from the Japanese Fisheries Agency.

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Table 1. Number of sei whale samples used in the analyses.
Maturity stage

2002

2003

2004

2005

2006

2007

3
12
3
2
17

6
16
4
2
15
3
47

11
27
11
5
25
5
85

6
40
14
4
28
1
94

16
29
11
5
24
7
92

15
30
15
4
15
1
80

Male immature
Male mature
Female immature
Female maturePregnant
Pregnant lactating
Total

37

Year
2008 2009
10
33
16
2
32
1
95

12
33
9
9
32
1
97

2010

2011

2012

2013

2014

14
28
10
8
35
4
99

11
40
12
7
15
2
87

11
31
10
7
35
1
96

15
28
6
17
30
2
98

10
25
9
6
28
5
83

2008
6
23
4
7
9

2009
9
8
14
7
9
2
49

2010
5
19
4
6
15

2011
10
10
11
8
11

49

50

2012
5
6
4
8
10
1
34

2015 Total
10
15
14
11
26
4
80

150
387
144
89
357
37
1170

Table 2. Number of Bryde’s whale samples used in the analyses.
2000
Male immature
Male mature
Female immature
Female maturePregnant
Pregnant lactating
Total

5
5
9
1
20

2001
13
4
12
2
10
9
50

2002
17
7
11
3
10
1
49

2003
10
9
9
7
10
4
49

2004
5
14
7
2
21
49

2005
10
11
12
6
7
4
50

Year
2006 2007
8
13
13
10
7
5
9
6
11
15
1
48
50

49

2013 Total
3
114
9
143
3
108
4
80
7
154
1
24
27
623

Table 3. Number of common minke whale samples used in the analyses.
Maturity Stage
Male immature
Male mature
Female immature
Female maturePregnant
Pregnant lactating
Total

1994
1
17
2
0
1

1995
2
89
2
0
7

1996
11
52
5
2
7

1997
16
71
7
0
6

1998
15
74
6
1
4

1999
12
59
13
1
14

2000
10
25
1
1
3

2001
15
78
4
0
3

2002
24
93
20
2
7

2003
25
89
21
1
13

Year
2004 2005
21
46
102
93
12
45
2
3
6
17

2006
47
89
40
1
8

21

100

77

100

100

99

40

100

146

149

143

185

204

2007
44
88
54
0
11
1
198

2008
43
60
43
3
12
1
162

2009
56
37
56
1
4

2010
44
26
45
0
2

2011
33
49
33
6
5

2012
69
38
56
2
9

2013
29
32
27
3
3

2014
31
20
22
0
4

154

117

126

174

94

77

2015 Total
9
603
1281
514
0
29
1
147
2
10 2576

Table 4 Names of variables and terms used in the regression analyses.
Response variable(with sample size)

BT 11
BT 7
UmbilicusGirth
AxillaryGirth
FatWeight

Blubber thickness at mid-lateral point on the vertical axis of the dorsal fin (in cm)
Blubber thickness at a mid-lateral position on the vertical axis of the umbilicus (in cm)
Half girth at the level of the umbilicus (in cm)
Half girth at the level of the axilla (in cm)
Weight of subcutaneous fat (blubber) + weight of intestinal fat (in metric tons)

Explanatory variable YearNum(continuous)
BLm
DateNum
LongNum
LatNumLtimeNum
wtemp

Year as a continuous variable
Body length (in m)
Date number (1 April = day 1)
Longitude in degrees E
Latitude in degrees N
Local time of day
Near surface water temperature (°C)

Explanatory variable YearCat(categorical)
LatCatLongCat
PreMaturity_Cat
OJ75

Year as a categorical variable
Latitude divided into 5 intervals
Longitude divided into 5 sectors
Maturity stage based on theobservation of internal organs
Stock difinition by genetic infromation for common minke whales with confidence level at 75% (Pastene et al., JR39)

Interaction andrandom effects

A:B
Interaction between A and B(1|YearCat)
Random effects of year on the model Intercept (assuming randomness of slope and intercept among years)( DateNum|LatCat) Random effects of DateNum partitioned by LatCat( DateNum|LongCat)Random effects of DateNum partitioned by LongCat

8

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Table 5 The full model and best model for each response variable.
Responsemodel type The response variables of each model
2
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat
Full model+ PreMaturity_Cat + MainPreyS + BLm:DateNum + BLm:YearNum + BLm:PreMaturity_Cat + DateNum:YearNum
BT11+ DateNum:PreMaturity_Cat + YearNum:PreMaturity_Cat + DateNum:BLm:PreMaturity_Cat + DateNum:BLm:YearNum
2
Best model YearNum + BLm + DateNum + FirstS + wtemp + PreMaturity_Cat +(1 | YearCat) + DateNum:BLm + YearNum:DateNum + DateNum:PreMaturity_Cat
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat + PreMaturity_Cat
Full model
BT7+ MainPreyS + BLm:PreMaturity_Cat + BLm:F_lat + F_lat:PreMaturity_Cat+ BLm:F_lat:PreMaturity_Cat
Best model BLm + DateNum + F_lat + FirstS + wtemp + PreMaturity_Cat + BLm:F_lat + (1 | YearCat)
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat
Full model
Umbilicus Girth+ PreMaturity_Cat + MainPreyS + BLm:PreMaturity_Cat + (1|YearCat)
Best model BLm + DateNum + FirstS + wtemp + PreMaturity_Cat + (1 | YearCat)
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat + PreMaturity_Cat
Full model
Axillary Girth+ MainPreyS + BLm:PreMaturity_Cat + BLm:YearNum + YearNum:PreMaturity_Cat
Best model YearNum + BLm + DateNum + FirstS + wtemp + PreMaturity_Cat + YearNum:BLm + (1 | YearCat)

Term (A:B) means interaction of “A” and “B”.

Table 6. Summary for the best model using BT11 of sei whales as the response variable
Random effects:
GroupsName
YearCat(Intercept)
Residual
Number of obs: 1152, groups: YearCat, 14

Variance
0.042140.74552

Std.Dev.
0.20530.8634

Std. Error
75.470
0.038
0.005
0.652
0.000
0.012
0.479
0.406
0.754
0.367
0.347
0.001
0.000
0.004
0.004
0.007
0.003
0.003

t value-2.906
2.908
4.831
2.875
-1.715-1.242
3.113
1.705-2.444-0.617-2.408-3.151-2.787-2.8931.646
0.748-0.215
1.853

Fixed effects:(Intercept)
YearNum
I(BLm^2)
DateNum
FirstS
wtemp
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]
DateNum:BLm
YearNum:DateNum
DateNum:PreMaturity_Cat[T.F_mat]
DateNum:PreMaturity_Cat[T.F_preg]
DateNum:PreMaturity_Cat[T.F_preglac]
DateNum:PreMaturity_Cat[T.M_immat]
DateNum:PreMaturity_Cat[T.M_mat]

Estimate-219.300
0.109
0.022
1.876
-0.001-0.0151.490
0.692-1.842-0.227-0.835-0.003-0.001-0.0130.006
0.005-0.0010.006

9

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Table 7 Summary for the best model using BT7 of sei whales as the response variable
Random effects:
GroupsName
YearCat(Intercept)
Residual
Number of obs: 1153, groups: YearCat, 14

Variance
Std.Dev.
0.03750.19360.381
0.6172

Fixed effects:(Intercept)
BLm
DateNum
F_lat
FirstS
wtemp
PreMaturity_CatF_mat
PreMaturity_CatF_preg
PreMaturity_CatF_preglac
PreMaturity_CatM_immat
PreMaturity_CatM_mat
BLm:F_lat

Estimate
Std. Errort value-14.550
4.044-3.597
1.179
0.292
4.033
0.007
0.002
4.414
0.395
0.098
4.026
-0.0010.000-2.546
0.004
0.011
0.393
0.044
0.099
0.444
0.822
0.081
10.119-0.6490.128-5.093-0.2650.073-3.609-0.0520.069-0.749-0.0260.007-3.688

Table 8 Summary for the best model using umbilicus girth of sei whales as the response variable
Random effects:
GroupsName
Variance Std.Dev.
YearCat(Intercept)
19.68
4.437
Residual
166.8912.919Number of obs: 1145, groups: YearCat, 14
Fixed effects:(Intercept)
BLm
DateNum
FirstS
wtemp
PreMaturity_CatF_mat
PreMaturity_CatF_preg
PreMaturity_CatF_preglac
PreMaturity_CatM_immat
PreMaturity_CatM_mat

Estimate
Std. Error t value
15.8237.893
2.000
17.4690.552
31.670
0.169
0.018
9.410
0.010
0.005
2.090
0.156
0.177
0.880
-2.0572.075-0.99019.503
1.699
11.480-16.3412.698-6.060-5.2381.539-3.400-4.5241.450-3.12010SC/F16/JR27

Table 9 Summary for the best model using axillary girth of sei whales as the response variable
Random effects:
GroupsName
Variance Std.Dev.
YearCat(Intercept)
35.69
5.974
Residual
185.46
13.618
Number of obs: 1133, groups: YearCat, 14
Fixed effects:
Estimate Std. Error t value(Intercept)-4397.000 2769.000
YearNum
2.183
1.379
BLm
726.400
192.000
DateNum
0.141
0.019
FirstS
0.003
0.005
wtemp
-0.071
0.188
PreMaturity_CatF_mat-0.256
2.233
PreMaturity_CatF_preg
15.7201.809
PreMaturity_CatF_preglac-12.710
2.825
PreMaturity_CatM_immat-4.704
1.640
PreMaturity_CatM_mat-4.101
1.542
YearNum:BLm-0.3510.096-1.588
1.584
3.784
7.258
0.638
-0.381-0.1158.689-4.499-2.868-2.659-3.671

Table 10 Summary of the full and best models for each response variable in Bryde’s whale.
Response
BT11model type The independent variables of each model
Full model

YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat+ PreMaturity_Cat + MainPreyS + PreMaturity_Cat:F_long + (1|YearCat)

Best model BLm + DateNum + FirstS + wtemp + PreMaturity_Cat + (1 | YearCat)
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat
Full model+ PreMaturity_Cat + MainPreyS + BLm:F_long + PreMaturity_Cat:F_long + YearNum:F_long
BT7+ BLm:F_long:YearNum + (1|YearCat) + (DateNum|LatCat)
Best model BLm + DateNum + FirstS + PreMaturity_Cat + (1 | YearCat) + (DateNum | LatCat)
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp + LatCat + LongCat
Full model
Umbilicus Girth+ PreMaturity_Cat + MainPreyS +BLm:PreMaturity_Cat + PreMaturity_Cat:YearNum+ (1|YearCat)
Best model BLm + DateNum + FirstS + wtemp + PreMaturity_Cat + (1 | YearCat)
YearNum + BLm + DateNum + F_lat + F_long + FirstS + wtemp 2 + LatCat + LongCat
Full model
Axillary Girth+ PreMaturity_Cat + MainPreyS + YearNum:BLm + (1|YearCat)
Best model YearNum + BLm + FirstS + I(wtemp^2) + PreMaturity_Cat + (1 | YearCat) + YearNum:BLm

Table 11 Summary for the best model using BT11 of Bryde’s whales as the response variable.
Random effects
Groups
YearCatResidual

Name
Variance
Std.Dev.
(Intercept)
0.082
0.286
0.6625
0.814

Fixed effects(Intercept)
BLm
DateNum
FirstS
wtemp
PreMaturity_Cat[T.F_immat]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]

Estimate
Std.Error
t-value
1.346
0.927
1.452
0.117
0.049
2.409
0.008
0.003
2.97
0.000
0.000
-0.284
0.040
0.022
1.813
0.262
0.596
0.44
0.321
0.590
0.544
1.221
0.585
2.086
-0.0400.618-0.065-0.0140.596-0.023-0.0800.586-0.13611

SC/F16/JR27

Table 12 Summary for the best model using BT7 of Bryde’s whales as the response variable.
Random effects:
Groups
YearCat
LatCatName(Intercept)(Intercept)
DateNum

Residual
Number of obs: 506, groups: YearCat, 14; LatCat, 5

Variance
Std.Dev.
Corr
0.085
0.292
0.033
0.182
0.000
0.001
0.383
0.618

-1

Fixed effects:(Intercept)
BLm
DateNum
FirstS
PreMaturity_Cat[T.F_immat]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]

Estimate
Std. Errort value
2.221
0.677
3.282
0.084
0.037
2.247
0.009
0.002
4.648
0.000
0.000
0.052
-0.3520.456-0.772-0.0650.450-0.143
0.414
0.446
0.929
-0.4310.471-0.914-0.5040.456-1.107-0.1920.447-0.431

Table 13 Summary for the best model using umbilicus girth of Bryde’s whales as the response variable
Random effects:
Groups
YearCatResidual
Number of obs: 505, groups: YearCat, 14

Name(Intercept)

Variance
43.110168.470

Std.Dev.
6.566
12.979Estimate-18.268
16.199
0.125
0.010
0.591
31.58837.97749.73531.90031.12331.902

Std. Error
14.875
0.774
0.045
0.007
0.353
9.514
9.429
9.346
9.879
9.514
9.351

t value-1.22820.940
2.780
1.475
1.671
3.320
4.028
5.322
3.229
3.271
3.412

Fixed effects:(Intercept)
BLm
DateNum
FirstS
wtemp
PreMaturity_Cat[T.F_immat]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]

Table 14 Summary for the best model using axillary girth of Bryde’s whales as the response variable
Random effects:
GroupsName
YearCat(Intercept)
Residual
Number of obs: 503, groups: YearCat, 14

Variance269.500
189.100

Std.Dev.
16.42013.750Std. Error
3658.000
1.824
242.300
0.007
0.008
10.09010.0009.911
10.50010.0909.919
0.121

t value-2.115
2.110
6.805
1.097
2.494
3.097
4.081
4.800
3.104
2.963
3.494
-6.722

Fixed effects:(Intercept)
YearNum
BLm
FirstSI(wtemp^2)
PreMaturity_Cat[T.F_immat]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]
YearNum:BLm

Estimate-7739.000
3.848
1649.000
0.008
0.020
31.23040.81047.58032.59029.89034.650-0.812

12

SC/F16/JR27

Table 15 Summary of the full and best models for each response variable in common minke whale fromJARPN and JARPN II period (1994-2015).
Responsemodel type The independent variables of each model
YearNum + BLm + DateNum + F_lat + F_long + PreMaturity_Cat + LatCat + LongCat + OJ75 + BLm:DateNum +BLm:LongCat + BLm:DateNum + BLm:LongCat + YearNum:DateNum + F_long:LongCat + BLm:DateNum:LongCat +Full model
BLm:DateNum:F_long + BLm:DateNum:YearNum + BLm:LongCat:DateNum + YearNum:DateNum:F_long +BT11
YearNum:DateNum:LongCat + BLm:LongCat:YearNum + (1|YearCat)
YearNum + BLm + DateNum + F_lat + F_long + PreMaturity_Cat +Best model
LatCat + OJ75 + BLm:DateNum + YearNum:DateNum + YearNum:BLm:DateNum + (1 | YearCat)
2
YearNum + BLm + DateNum + F_lat + F_long + PreMaturity_Cat + LatCat + LongCat + OJ75 + PreMaturity_Cat +BLm:PreMaturity_Cat + BLm:DateNum + BLm:LongCat + YearNum:DateNum + F_long:DateNum + F_long:LongCat
Full model
BT7+ BLm:DateNum:LongCat + BLm:DateNum:F_long + BLm:DateNum:YearNum + BLm:LongCat:DateNum +YearNum:DateNum:F_long + YearNum:DateNum:LongCat + BLm:LongCat:YearNum + (1|YearCat)
Best model YearNum + BLm2 + DateNum + PreMaturity_Cat + OJ75 + DateNum:BLm + YearNum:DateNum + (1 | YearCat)
YearNum + BLm + DateNum + F_lat + F_long + PreMaturity_Cat + LatCat + LongCat
Full model+ OJ75 + PreMaturity_Cat + BLm:PreMaturity_Cat +BLm:DateNum + PreMaturity_Cat:DateNum
Umbilicus Girth+ BLm:DateNum:PreMaturity_Cat + (1 | YearCat)
Best model YearNum + BLm + DateNum + F_long + PreMaturity_Cat + LatCat + OJ75 + YearNum:BLm + (1 | YearCat)
YearNum + BLm + DateNum + F_lat + F_long + LatCat + LongCat + OJ75 + PreMaturity_Cat + BLm:PreMaturity_Cat
Full model
Axillary Girth+ (1|YearCat)
Best model YearNum + BLm + DateNum + OJ75 + PreMaturity_Cat + BLm:PreMaturity_Cat + (1 | YearCat)

Table 16 Summary for the best model using BT11 of common minke whales as the response variable
Random effects:
Groups
YearCatResidual
Number of obs: 2104, groups: YearCat, 20

Name(Intercept)

Variance
0.066
0.404

Std.Dev.
0.258
0.636

Estimate
106.600-0.051-0.2840.212
0.083-0.015-0.193
0.427
0.863
0.135
-0.281-0.067-0.321-0.416-0.601-0.2250.304-0.2100.000
0.000

Std. Error
29.110
0.015
0.040
0.406
0.029
0.003
0.108
0.166
0.104
0.462
0.104
0.086
0.086
0.131
0.196
0.271
0.047
0.056
0.000
0.000

t value
3.661-3.537-7.1300.522
2.855-4.903-1.789
2.571
8.310
0.292
-2.700-0.781-3.738-3.165-3.063-0.8286.449-3.783-0.5693.849

Fixed effects:(Intercept)
YearNum
BLm
DateNum
F_lat
F_long
PreMaturity_Cat[T.F_immat]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]
LatCat[T.2]
LatCat[T.3]
LatCat[T.4]
LatCat[T.5]
OJ75[T.O]
BLm:DateNum
YearNum:DateNum
YearNum:BLm:DateNum

13

SC/F16/JR27

Table 17 Summary for the best model using BT7 of common minke whales as the response variable
Random effects:
Groups
YearCatResidual
Number of obs: 2104, groups: YearCat, 20

Name(Intercept)

Variance
0.049
0.257

Std.Dev.
0.222
0.507

Estimate
42.280-0.019-0.020-0.592-0.165
0.512
0.759
0.035
-0.262-0.080
0.285
0.003
0.000

Std. Error
23.760
0.012
0.002
0.142
0.087
0.132
0.082
0.366
0.084
0.068
0.037
0.000
0.000

t value
1.779-1.640-8.344-4.169-1.905
3.884
9.243
0.094
-3.105-1.1657.718
15.188
4.064

Fixed effects:(Intercept)
YearNum
I(BLm^2)
DateNum
PreMaturity_Cat[T.F_immat]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]
OJ75[T.O]
DateNum:BLm
YearNum:DateNum

Table 18 Summary for the best model using umbilicus girth of common minke whales as the response
variableRandom effects:
Groups
YearCatResidual
Number of obs: 2379, groups: YearCat, 20

Name(Intercept)

Variance
26.04094.790

Std.Dev.
5.103
9.736

Estimate
64.30019.000-0.258-0.14292.14649.438155.338-6.92770.176-0.913-12.027-5.262-21.790
1.316-9.5780.048

Std. Error
6.353
0.670
0.021
0.035
32.38916.304374.253
4.486
7.531
0.636
4.100
2.111
51.154
0.807
1.089
0.003

t value
10.12128.374-12.334-4.053
2.845
3.032
0.415
-1.5449.319-1.434-2.933-2.493-0.4261.630-8.79614.190

Fixed effects:(Intercept)
BLm
DateNum
F_long
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]
OJ75[T.O]
BLm:PreMaturity_Cat[T.F_mat]
BLm:PreMaturity_Cat[T.F_preg]
BLm:PreMaturity_Cat[T.F_preglac]
BLm:PreMaturity_Cat[T.M_immat]
BLm:PreMaturity_Cat[T.M_mat]
BLm:DateNum

14

SC/F16/JR27

Table 19 Summary for the best model using axillary girth of common minke whales as the response
variableRandom effects:
Groups
YearCatResidual
Number of obs: 2360, groups: YearCat, 20

Name(Intercept)

Variance
9.341
91.176

Std.Dev.
3.056
9.549

Estimate
7.870
24.446
0.021
0.312
0.576
40.416126.855-3.18253.5890.139-4.078-16.579
0.492-7.241

Std. Error
3.340
0.585
0.004
0.622
31.71315.982366.992
4.413
7.393
4.013
2.070
50.1620.793
1.067

t value
2.360
41.810
5.060
0.500
0.020
2.530
0.350
-0.7207.250
0.030-1.970-0.3300.620-6.780

Fixed effects:(Intercept)
BLm
DateNum
OJ75[T.O]
PreMaturity_Cat[T.F_mat]
PreMaturity_Cat[T.F_preg]
PreMaturity_Cat[T.F_preglac]
PreMaturity_Cat[T.M_immat]
PreMaturity_Cat[T.M_mat]
BLm:PreMaturity_Cat[T.F_mat]
BLm:PreMaturity_Cat[T.F_preg]
BLm:PreMaturity_Cat[T.F_preglac]
BLm:PreMaturity_Cat[T.M_immat]
BLm:PreMaturity_Cat[T.M_mat]

15

SC/F16/JR27

Figure 1. Map of JARPN II research area (grey highlighted). Areas 7, 8 and 9 are defined by IWC (1994).

Figure 2. Position of blubber thickness and half girth measurements. Closed circles:
Lateral points for blubber thickness measurements. Open triangle: position of theumbilicus. Arrows: half girth at the levels of the axilla and the umbilicus. (Cited from
Konishi and Walløe, in press)

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Figure 3. Dendrogram of only fixed effects model for BT11 in sei whale.

Figure 4. Dendrogram of only fixed effects model for BT7 in sei whale.

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Figure 5. Dendrogram of the basic model for umbilicus girth in sei whale.

Figure 6. Dendrogram of the basic model for axillary girth in sei whale.

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Figure 7. Interaction effect of YearNum and DateNum on BT11 in sei whale.

Figure 8. Interaction effect of BLm and DateNum on BT11 in sei whale. Body length was defined by
0,25,50,75,100 percentile.

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Figure 9. Interaction effect of PreMaturity_Cat and DateNum in the best model for BT11 (PreMaturity_Cat
and DateNum) in sei whale. (F: female, M: male, immat: immature, mat: mature, preg: pregnant, preglac:
pregnant and lactating).

Figure 10. Dendrogram of only fixed effects model for BT11 in Bryde’s whale.

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Figure 11. Dendrogram of only fixed effects model for BT7 in Bryde’s whale.

Figure 12. Dendrogram of only fixed effects model for umbilicus girth in Bryde’s whale.

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Figure 13. Dendrogram of only fixed effects model for axillary girth in Bryde’s whale.

Figure 14. Dendrogram of only fixed effects model for BT11 in common minke whale.

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Figure 15. Dendrogram of only fixed effects model for BT7 in common minke whale.

Figure 16. Dendrogram of only fixed effects model for umbilicus girth in common minke whale.

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Figure 17. Dendrogram of only fixed effects model for axillary girth in common minke whale.

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APPENDIX 1
SCATTER PLOTS AND DIAGNOSTIC PLOTS FOR THE BEST MODELS IN SC/F16/JR27

Figure 1. Scatter plots of predictor variables in sei whales sampled in JAPPN II cruise.

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Figure 2. Diagnostic plots of the best model in BT11 of sei whale.

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Figure 3. Diagnostic plots of the best model for BT7 in sei whale.

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Figure 4. Diagnostic plots of the best model for umbilicus girth in sei whale.

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Figure 5 Diagnostic plots of the best model for axillary girth in sei whale.

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Figure 6. Scatter plots of predictor variables in Bryde’s whales sampled in JAPPN II cruise.

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Figure 7. Diagnostic plots of the best model for BT11 in Bryde’s whale.

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Figure 8. Diagnostic plots of the best model for BT7 in Bryde’s whale.

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Figure 9. Diagnostic plots of the best model for umbilicus girth in Bryde’s whale.

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Figure 10. Diagnostic plots of the best model for axillary girth in Bryde’s whale.

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Figure 11. Scatter plots of predictor variables in common minke whales sampled in JARPN and JAPPN II
cruise.

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Figure 12. Diagnostic plots of the best model for BT11 in common minke whale.

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Figure 13. Diagnostic plots of the best model for BT7 in common minke whale.

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Figure 14. Diagnostic plots of the best model for umbilicus girth in common minke whale.

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Figure 15. Diagnostic plots of the best model for axillary girth in common minke whale.

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