SC/65b/EM03

Responses to some of the conclusions and recommendations
from the JARPAII Review Panel for stomach contents
analyses (SC/F14/J14) regarding to long-term trend ofstomach content weight in the Antarctic minke whale
KENJI KONISH IAND LARS WALLØE2
1

Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055, Japan2
Department of Physiology, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1103 Blindern,
N-0317 Oslo, Norway

ABSTRACTThe authors presented a paper showing a decline of stomach contents throughout JARPA and JARPA II period in theJARPA II Review Workshop. In the present paper, the authors have performed some additional regression analyses
with diagnostic plots that were recommended by the Review Panel, but only for the JARPA period. In the originalpaper, which is now published in a journal (Konishi et al. 2014 as SC/65b/Forinfoxx), the best model was selected by
the use of AIC. The best model selected by BIC also has a negative year effects, supporting the finding of a declineof stomach content weight throughout the JARPA period as the authors have reported. The diagnostic plots did notshow any problem for the best model. The conclusion must be that the negative trend of stomach content weight in
the Antarctic minke whale is real and should be interpreted as a result of a decline in prey availability.
KEY WORDS: ANTARCTIC, ANTARCTIC MINKE WHALE, STOMACH CONTENTS, BIOLOGICALPARAMETER TRENDINTRODUCTION
The workshop to review the progress made in the research conducted under the Japanese Whale Research Program
under Special Permit in the Antarctic–Phase II (JARPAII) in its first six years (2005/06-2010/11) was carried out inFebruary 2014. During the workshop the authors presented a document (SC/F14/J14, later published as Konishi et al.
2014 as SC/65b/Forinfoxx) regarding to temporal trend of stomach content weight in the Antarctic minke whale. Inthe Review Panel Report of the JARPA II Special Permit Review Workshop (SC/65b/Rep02), comments andrecommendations on Document SC/F14/J14 were reported. Here the authors present detailed responses to thosecomments and recommendations. The comments on SC/F14/J14 from the Review Panels are summarized below:

Comments A‘Following the selection of which factors to consider in the modelling, the following steps should be undertaken:(1) identify whether any of the covariates are highly correlated and either (a) exclude a subset of the covariates
so that the remaining covariates are uncorrelated or (b) develop new covariates which represent
independent aspects of the current covariates (using for example PCA);(2) select a ‘full model’ (this may be difficult if the data set is unbalanced) and base selection of which factors
and their interactions to treat as random effects - the models should be fitted using REML and a model
selection approach such AIC, BIC or standard hypothesis testing approach applied; and(3) select the fixed effects structure given the random effects structure selected at step (2), where the models are
fitted using maximum likelihood;(4) use REML to fit the best model identified in (3) above.’

Comments B‘SC/F14/J13 and SC/F14/J14 do not report many fit diagnostics. The Panel recommends that any revised papers
provide at least plots of the residuals versus the predictor variables (including year and stratum), histograms ofresiduals and random effects, plots of residuals spatially, and Q-Q plots for the ‘best model’.’Comments C‘The Panel also recommends that future analyses of the data on the condition of Antarctic minke whales include (a)consideration of a model in which year is a categorical variable and is treated as a random effect if a plot ofresiduals against year show there are residual patterns by year,’The review panel also commented that it would like to see the results if BIC was used for model selection, since theauthors have used this criterion in their other paper to the review meeting.
The present paper presents the regression results according to the comments and recommendations above, but onlyfor the JARPA period, since possible changes in energy storage in minke whales during this period have been
intensively discussed in the SC during the last few years.
RESULTS AND DISCUSSION
Response to Comments A
To respond to comment on (1), the correlation matrix for the independent variables is provided in Appendix 1. The
matrix shows that all the other correlations between the independent variables have values less than about 0.2 (exceptthe correlation between longitude and latitude caused by the catches in the Ross Sea and between Latitude and Date).
For this reason the authors have not considered it necessary either to exclude any of the other variables or to develop
new independent variables by the use of principal component analysis (PCA).
According to comments on (2) to (4):a) We have followed the selection procedure as same as SC/65b/EM02. There is no objective ‘full model’
and the choice of the ‘full model’ is dependent on what one regards as relevant and plausible
assumptions. In the following the authors comment only on the modelling of stomach content weight
during the JARPA period, since this is the period which has been discussed repeatedly in the SC.
b) Because the AIC was used in the previous model selection in SC/F14/J14, we have this time mademodel selection by BIC with adding the random effects which were suggested by the Review Panel.
c) The random term should first have been explored as an interaction term. An basic full model islog(Stomach content weight) ~ Date2 + Latitude + Longitude + Year + Local time + Sex + Body +length(m)The authors have now performed these calculations for the interaction terms which were suggested by the SC in2013 and an interaction term which they found useful based on de la Mare et al.’s models from their paper
SC/F14/O6. The models and the results are presented in Appendix 2. This adjustment did not result in any importantchange in model selection (based on lowest BIC value) in any of the cases we have explored. The best model
selected by AIC in Konishi et al.2014 (SC/65b/Forinfoxx ) had larger BIC values both in the best model lm.sto1.1
for the data used in the published paper and for the JARPA data only as used in the present investigation, but the yeareffect and its SE are not very different (Appendix 2)." . The information that BIC should be preferred compared toAIC when the data set is large as in JARPA and JARPA II surveys was described in SC-F14-R5.

Response to Comments B
The authors have now developed the plots recommended by the Review panel for the best model. (Appendix 3).
These diagnostic plots show no particular problems for the best models.

Response to Comments C
As can be seen clearly from Appendix 3 the plots of residuals against year show no cyclic or otherwise disturbing
pattern for any of the models explored. For this reason the authors see no reason to consider a model in which year isa categorical variable and is treated as a random effect. Although the histogram of residuals for the best modelskewed to the left, the regression analyses as robustness trial using ranked and non-transformed stomach content
weight supported main regression results (SC/F14/J14 also SC/65b/Forinfoxx).
In conclusion, the negative trend of stomach contents in the Antarctic minke whale during the JARPA period is valid,supporting the previously reported synchronous decline of body condition during JARPA period (Konishi et al. 2008,
SC/F14/J13).

ACKNOWLEDGEMENTS
The authors acknowledge the JARPAII Review Panel for the useful suggestions to improve our study.
REFERENCES
SC/65b/Rep02 International Whaling Commission. Panel Report of the JARPA II Special Permit Review Workshop
SC/65b/SP01 Pastene, L., Watanabe, T., Kanda, T., Goto M., Hakamada, T., Murase H., Tamura, T., Konishi, K.,Bando, T., Mogoe, T. and Kitakado, T. Response to the ‘Panel Report of the JARPAII Special Permit ReviewWorkshop’.
SC/F14/J13. Konishi, K. and Walloe, L. Time trends in the energy storage in the Antarctic minke whales during theJARPA and JARPA II research periods.
SC/F14/J14 (also SC/65b/Forinfoxx). Konishi, K., Hakamada, T., Kiwada, H., Kitakado, T. and Walloe, L. Decreasein stomach contents in the Antarctic minke whale (Balaenoptera bonaerensis) in the Southern Ocean.
(published in Polar Biology 2014 37:205-215)
SC/F14/O06. de la Mare, W.K., Candy, S., McKinlay, J., Wotherspoon, S., Double, M. What can be concluded fromthe statistical analyses of JARPA/JARPA II body condition data?
SC-F14-R5. Konishi, K. and Walløe, L. Response to the paper SC/F14/O06: «What can be concluded from thestatistical analyses of JARPA/JARPA II body condition data?» by William de la Mare, Steven Candy,
John McKinlay, Simon Wotherspoon and Michael Double.
SC/65b/Forinfoxx. Konishi, K., Tamura, T., Zenitani, R., Bando, T., Kato, H. and Walløe, L. 2008. Decline inenergy storage in the Antarctic minke whale (Balaenopters bonarensis) in the Southern Ocean. 2008, PolarBiology 31(12):1509-1520.

Appendix 1
Correlations among independent variables

Pearson correlations:
BLm
BLm
DateNum
LatNumLongNum
LtimeNum
YearNum

DateNum LatNum LongNum LtimeNum YearNum
1.000
0.098
1.000
-0.203-0.378
1.000
0.036
0.173
-0.5111.000-0.014-0.0930.056-0.0701.000-0.034-0.092-0.091
0.057-0.0241.000

Appendix 2 The models and the results of models for Stomach content weight.
Results of regressions for stomach content weight during JARPA period (1990/91-2004/05)
Covariates of randomNo.
BIC Year effctSEtmodel
effects
1 13977 -0.020 0.007 -3.10 lm.sto1 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm)
0.103
YearNum-LatNum
0.116
0.89 lm.sto2 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + Year:LatNum)
2 13978-0.011
3 15087
0.007 -1.58 lmer.sto2 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (Year|LatNum), REML = T)
-0.783
LonSect-Year
0.361 -2.17 lm.sto3 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + LonSect:Year)
4 14116
0.021
5 141430.019
1.15 lmer.sto3 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (LonSect|Year), REML = T)
-0.020Date-LonSect
0.007 -3.01 lm.sto4 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + DateNum:LonSect)
6 13985-0.0207 14073
0.007 -2.97 lmer.sto4 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (DateNum|LonSect), REML = T)
-0.0200.007 -3.01 lm.sto5 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + DateNum:LongNum)
Date-LongNum
8 13985-0.0200.007 -3.07 lmer.sto5 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (DateNum|LongNum), REML = T)
9 14069-0.019YearNum-Ice
0.008 -2.52 lm.sto6 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + YearNum:Ice)
10 13985-0.013
11 14069
0.020 -0.64 lmer.sto6 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (YearNum|Ice), REML = T)
-0.028Year-LtimeNum
12 13991
0.022 -1.28 lm.sto7 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + Year:LtimeNum)-0.02313 15124
0.008 -2.99 lmer.sto7 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (Year|LtimeNum), REML = T)
-0.028
LtimeNum-Year
14 13991
0.022 -1.28 lm.sto8 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + LtimeNum:Year)same as lm.sto7-0.01615 14002
0.018 -0.89 lmer.sto8 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (LtimeNum|Year), REML = T)
0.002
YearNum-Lat70
16 13979
0.011
0.22 lm.sto9 <- lm(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + YearNum:Lat70)-0.01417 14071
0.011 -1.25 lmer.sto9 <- lmer(log(FirstS) ~ I(DateNum^2) + LatNum + LongNum + YearNum + LtimeNum + Sex + BLm + (YearNum|Lat70), REML = T)Best model from-0.0260.007 -3.93 LMER24<- lmer(log(FirstS)~(I(DateNum^2)-1|LatCat11:Year)+(I(DateNum^2)-1|LongCat11:Year)+YearNum + LtimeNum + BLm + Sex, REML = T) #18 14056
Konishi et al. (2014)
Modified lm.sto1

1913969-0.0200.007 -3.10 lm.sto1.1 <- lm(log(FirstS) ~ I(DateNum^2) + LongNum + YearNum + LtimeNum + Sex + BLm) #lm.sto1 - LatNum

Results of Modified lm.sto1 and the Fixed effects in the best model based on Konishi et al (2014) during JARPA periodlm.sto 1
Fixed effects of the best model in Konishiet al. (2014)

Residuals:
M in

1Q M edian

3QM ax-5.4882 -1.1189 0.3483 1.2780 3.6083
Coefficients: Estimate

Std. Errort value

Pr(>|t|)(Intercept)
2DateNum

1.37E+00
3.94E-05

0.269
0.000

5.086
4.452

3.85e-07 ***8.76e-06 ***LongNum
YearNum
LtimeNum-3.96E-03-2.03E-02-1.24E-01

0.001
0.007
0.008

-6.614-3.103-16.163

4.29e-11 ***0.00193 **< 2e-16 ***Sex[T.M]

2.41E-01

0.056

4.316

BLm

3.29E-01

0.027

12.153

Estimate

Std. Error

1.04E+00---

t value

0.259

4.021
--

-

-2.57E-02-1.24E-01

0.007
0.008

1.63e-05 ***2.46E-01

0.056

4.398

< 2e-16 ***3.32E-01

0.027

12.1995-3.929-16.126

Appendix 3 Diagnosis plots for the model (Modified lm.sto = lm.sto1.1)

6

Residual plots against year with locally weighted scatterplot smoothing
for the best model (lm.sto1.1)7