Addendum of the NEWREP-NP proposed revised research plan
Toshihide Kitakado1 and Luis A. Pastene2

1

Tokyo University of Marine Science and Technology, Konan 5-7-4, Minato-ku, Tokyo 108-8477, Japan

2

Institute of Cetacean Research, Toyomi-cho 4-5, Chuo-ku, Tokyo 104-0055, Japan

NOTE: this document assembles some additional information of the proposed revised research plan for
NEWREP-NP. Specifically the additional information is as follows:Basis and analytical methods related to the selection of the sample size for common minke whales(Annex 11 and section 3.1.3 of the research plan)Basis and analytical methods related to the selection of the sample size for sei whales (Annex 16 andsection 3.2.3 of the research plan)Assessment of the potential effect of catches on the stocks of minke and sei whales (sections 4.1 and4.2 of the research plan)

1

ANNEX 11
Selection of sample size for the North Pacific common minke whale
General background and rationale for the sample size on the Pacific side of Japan (sub-areas 7-9)
The SCAA assessment of Antarctic minke whale populations by Punt et al. (2014) was a watershed advance for
the IWC SC because, through its ability to take account of age in addition to survey abundance data, it pointed
to the extent of recruitment changes1 that could occur, and its results did not conform particularly closely to the
behaviour predicted by the standard population models used to assess and hence to provide baseline ISTs forbaleen whale populations. Figure 1 contrasts the results from an application by GOJ (2016) of the Punt et al.
SCAA methodology to those that would follow from a FITTER approach necessitated if only catch and survey
abundance information were available (as required for the RMP).

1

Recruitment refers to the numbers of young whales added to the population each year (also called a‘cohort’). This cannot be determined well if only a series of abundance estimates of the whole population areavailable. The availability of age data, however, allows estimates of total population numbers to be split into
the numbers of each cohort present that year. From one survey only, such estimates would not be precise, but
the accumulation of age data over successive years allows for multiple estimates of the size of each cohort,and it is effectively the combination of these which ultimately allows for reasonable estimates of annual
recruitment to be obtained.

2

Figure 1: Two approaches to conditioning potential ISTs for the I stock of Antarctic minke whales are compared. The firstuses the conventional approach for baseline trials in RMP Implementations, with only past catch and survey abundance
estimates (in this instance from the IDCR-SOWER cruises) available, and is calculated here using the FITTER-with-fixedMSYR methodology. The second uses the SCAA approach of Punt et al. (2014), as implemented by GOJ (2016), which canin addition take age data into account. Results are shown for the 1+ population trajectory for two different values ofMSYR(1+). The very different perception of the dynamics of the population that follows once age data are available for use
in the conditioning, and show that catches have not been the primary determinant of the population’s behaviour, is readily
evident.

The considerable difference is obvious; self-evidently optimal management based the scenario (and associatedsensitivities) provided by the SCAA, which can estimate recruitment directly through the availability of age
data, would be very different to that from the deterministic stock-recruitment relationship scenarios (as, e.g., the
FITTER methodology has to assume), which at best would need to consider a very wide range of robust tests,
resulting in an inefficient approach (less allowable catch for the same perceived risk).
The Punt et al. (2014) analysis constitutes an important step in contributing to the evolution of the RMP towards
a more efficient version which is based on better conditioned operating models, and is stock specific (as are thevarious current AWMPs) rather than generic as at present. Age data contribute to this better conditioning
through allowing much improved estimation of recruitment and its changes and may also be able to improve theperformance of a refined version of the RMP, as has been demonstrated in the case of Antarctic minke whales(GOJ, 2016). The NEWREP-NP proposal, with its analyses, has the intent that the age data to be collected willcontribute to this evolutionary process.
The JARPN II Final review workshop report, endorsed by the IWC SC, noted that ‘if the Implementation
Simulation Trials (ISTs) for the western North Pacific minke whales are to be revised in future, the age data
should be included in the conditioning process’ (SC/66b/Rep06, Report of the Expert Panel of the final review
on the western North Pacific Japanese Special Permit program (JARPN II), 4.4.1). The example above showsthat age data, whenever potentially available, are needed for conditioning such trials so that recruitment and itschanges may be reflected far better. This is the primary reason why the proponents support the use of age data
for the conditioning of the next set of ISTs for the North Pacific common minke whale, which they understandto be endorsed also by the IWC SC. Naturally recruitment is hardly estimable for other than past years spannedby the collection of age data, so for future sets of ISTs also to best reflect underlying dynamics, age data must
continue to be collected, notwithstanding the fact that the impact of data from the first few years of NEWREPNP to the next NP common minke whale Implementation Review may not be that large.

3

The proponents’ approach is entirely in line with fisheries management approaches elsewhere, including in thedevelopment of MPs in other Regional Fisheries Management Organizations (RFMO). There a high premium isplaced on obtaining and improving age data and/or on equivalent information to provide information on
recruitment changes. Further comments on this and other aspects of the use of age data in fisheries management
may be found in Adjunct 1. Furthermore Adjunct 2 provides an example of how the availability of age data aids
the estimation of the extent of the impact of environmental factors on recruitment trends – a matter ofimportance at this time given concerns about the possible impacts of Climate Change.
Note that while age data could be used in a future RMP in a similar way to that in the proposal in Governmentof Japan (2016), the primary contribution of such data remains to the conditioning of ISTs, and (as has proven to
be the preferred approach for other MPs internationally) their contribution to feedback adjustments tomanagement measures might be through the regular re-conditioning of the ISTs rather than by changes to the
MP itself.
Moving to the matter of sample size, it is perhaps helpful to first summarise the proponents’ rationale for thenumber advanced, before elaborating upon it in more detail. This rationale is that: Age data are needed for improved conditioning of ISTs for testing management procedures, to informbetter on recruitment changes and hence improves the trials’ realism

Simulation results (see Adjunct 2) indicate that larger age samples would allow better estimation ofrecruitment changes for this NP minke situationOn the other hand, operational considerations regarding the practically maximum sample size and theeffect on the population must also be taken into account in determining the optimal sample sizeTherefore, the optimal sample size should meet both of these criteria: that it is operationally maximal
and is also sufficient to provide meaningful improvement in the estimation of recruitment changes;
simulation results (see Adjunct 3) indicate that is the case for this NP minke situation. (The matter ofeffect on the population is dealt with in Section 4.1 of the main text of Revised Research Plan.)To elaborate then, given the clear and widely accepted benefits in principle of the inclusion of ageing data to the
IST conditioning process, the only question that then remains is how much age data is needed to make ameaningful improvement to that NP minke whale conditioning. A detailed calculation for this would need to be
based on the planned updated conditioned (including with the age data available at that time) set of NP minke
ISTs, and consequently would need to await completion of that exercise which is the responsibility of the IWC
SC.
However, in the interim, much simpler computations are adequate to bound the problem, and are conducted in
Adjunct 3. These are based on a simpler model broadly accepted when presented to the JARPN II review, which
was intended to be illustrative and to assist this bounding.
Note first that the model showed performance improved with increases in the sample size aged, and that these
improvements are meaningful over the sample sizes examined which were consistent with what was
operationally practical2. This last consideration then provides the desirable sample size, but always provided thata) the criterion of no adverse effect on the population is met, and b) that sample size is itself sufficient toprovide a meaningful improvement in performance. The intent of the calculations of Adjunct 3 is to address thislast question, and this is successfully achieved – note that this is an exercise for which primarily only relative
measures of performance when comparing results with to those without ageing data are needed. Once theupdated conditioning is complete, that could be used to update these overall results, though any difference
would not be expected to be large, and the priority for such an update would not seem to be very high, andresults from this bounding an illustrative exercise are sufficient to address the immediate question.
Given the relatively slow dynamics of minke whales, coupled to the nature of the information content of age
data, the improvements to ISTs achieved by use of these data take time to reveal their full extent (see the plots in
Adjunct 3), so that there is a need to show results for projections over a number of decades, extending beyond
2

Based on the scientific knowledge on minke whale distribution around Japan, estimated sampling efforts
given the available research vessels (see Annex 21 of the proposed proposal) and the allocation of efforts tothe two target species, annual sample size of 107 for minke whales was found to be optimal and feasible.

4the time-frame of the current research program. Self-evidently the results for these larger numbers of years must
be taken into account; otherwise the injudicious situation would arise that research with longer term benefits
would never commence because those benefits could never become evident in the short term.
In summary it is considered that the annual sample size of 107 minke whales in sub-areas 7-9, which is the
maximum feasible within the operational constraints of the program, is sufficient to result in meaningfulimprovement in the detection of minke whale recruitment changes.
This intended sample size applies to O stock whales. It is planned that 60% of this sample size be taken incoastal sub-areas (7CS and 7CN) and 40% in offshore sub-areas (7WR, 7E, 8 and 9). Evaluating an optimalcoastal:offshore ratio for this sample would be an enormous task technically, but it seems reasonable to expectthat a 50:50 split would be near optimal in terms of distinguishing possible differences between the two regions
if any. Taking into account operational reasons as well, the ratio has been decided to be 60:40, noting thattypically such “distinguishability” performance behaves quadratically, so does not deteriorate much withrelatively small movement away from the actual optimal split. Hence it is planned that 64 animals will besampled in coastal sub-areas and 43 in offshore sub-areas. Because around 20% of the animals in sub-areas 7CS
and 7CN are from the J stock (Annex 7), the sample size in the coastal sub-areas needs to be adjusted upwards
to 80 animals in total to achieve sampling of 64 O stock whales. Thus the total sample size planned on the
Pacific side of Japan becomes 123 whales.
Rationale for the sample size selected for the area north of Hokkaido (sub-area 11)
For the area north of Hokkaido (sub-area 11), the main objective is to estimate the J-O mixing proportion in thissubarea annually with a standard error of no more than 0.1 irrespective of the true proportion. The sample size
selected is 47. The basis for the selection of this value is explained in Adjunct 4.
Total planned sample size
With 123 whales to be taken on the Pacific side of Japan, and 47 north of Hokkaido, the total sample sizeplanned for common minke whales is 170.
References
Government of Japan. 2016. Results of the analytical works on NEWREP-A recommendations. Paper
SC/66b/SP10 presented to the IWC Scientific Committee. June 2016 (unpublished). 23pp.
Punt, A., Hakamada, T., Bando, T. and Kitakado, T. 2014. Assessment of Antarctic minke whales using
statistical catch-at-age analysis (SCAA). J. Cetacean Res. Manage. 14:93-116.

5

ADJUNCT 1
On the Use and Utility of Catch–at-Age Data in Marine Resource Assessment and Management
The inclusion of age data in fishery assessments is widespread in fishery management agencies worldwide,
including in Regional Fisheries Management Organisations (RFMOs). This use can also extend there to theprocess of developing management procedures (MPs), certainly for conditioning the operating models used fortesting those MPs, and sometimes directly in the MPs themselves. Generally a high premium is placed on
obtaining and improving age data and/or equivalent information to provide information on recruitment changes.
Examples of this in RFMOs are provided, for example, by:
CCAMLR: Further collection of age data for the assessment of toothfish stocks is recommended (e.g.
CCAMLR 2016).
CCSBT: Age data are used in conditioning the operating models used for MP selection for southern
bluefin tuna (SBT), and indirectly (through recruitment indices) in the MP itself (e.g. CCSBT 2016).
ICCAT: Age data are used in assessments of, for example, Atlantic bluefin tuna, and in thedevelopment of operating models for the MP in development for that resource (e.g. ICCAT 2014).
NAFO: Age data were used in conditioning the operating models for the MP previously adopted forGreenland halibut, and are similarly in use for the revision of this MP that is currently in progress (e.g.
NAFO 2010).
WCPFC: Age data are used in the assessments of various stocks, including bigeye tuna (e.g. Harley etal. 2014).
Many of the species involved above are long-lived, some to four decades which approaches the longevity ofmany whale species, so that dynamics, time scales, and management concerns are not dissimilar from those forwhales. One reason that perhaps increases the priority for ageing information for the species above compared towhales is its contribution towards estimation of abundance in absolute terms – whale sightings surveys provide
better approximations to this than are obtainable from abundance indices for many fish species. Nonetheless theprimary improvement provided by the availability of age data is the ability to assess year-class (recruitment)
strength and its variations. The identification of (series of) good and of poor recruitment plays an important rolein the management of these species despite their longevity, both as regards increasing and reducing catch limits.
Thus there is, for example, absolute unanimity in the CCSBT Scientific Committee on the need for recruitment
monitoring inputs in the MP used to recommend catch limits for SBT, following experience in that case of theconsequences of a run of poor recruitments across the turn of the century.
These same considerations apply to whales, where the absence of age data accordingly necessitates moreconservative management than might otherwise be necessary, i.e. lower catches for the same perceived risk.
The current IWC RMP relies (historical catches aside) on the input of survey based indices of abundance (withCVs) only. The assessment of the US Southern New England/Mid-Atlantic winter flounder (NEFSC 2011)provides a note of caution in this regard. If survey indices of abundance only were considered, that stock
appeared perfectly healthy; however a full assessment taking age data into account as well led to a different
appreciation, suggesting a resource appreciably reduced in abundance over recent decades. This points again to
the sound management of a marine resource requiring that age information (in addition to survey-based indicesin isolation) be obtained and taken into account whenever possible.

6References
Commission for the Conservation of Antarctic Marine Living Resources. 2016. Report of the Thirty-fifth
meeting of the Scientific Committee (Hobart, Australia, 17 to 21 October 2016) ,SC-CCAMLRXXXV. 481pp. Available at https://www.ccamlr.org/en/system/files/e-sc-xxxv.pdf
Commission for the Conservation of Southern Bluefin Tuna. 2016, Report of the Seventh Operating Model andManagement Procedure Technical Meeting, 3-4 September 2016, Kaohsiung, Taiwan. 22 pp. Available
athttps://www.ccsbt.org/sites/ccsbt.org/files/userfiles/file/docs_english/meetings/meeting_reports/ccsbt_
23/report_of_OMMP7.pdf
Harley, S. J., Davies, N., Hampton, J., and McKechnie, S. 2014. Stock assessment of bigeye tuna in the Western
and Central Pacific Ocean. Technical Report WCPFC-SC10-2014/SA-WP-01, WCPFC ScientificCommittee, Majuro. 115pp.
International Commission for the Conservation of Atlantic Tunas. 2014. Report of the 2014 Atlantic Bluefin
Tuna stock assessment session. Madrid, Spain. 178pp. Available athttps://www.iccat.int/Documents/Meetings/Docs/2014_BFT_ASSESS-ENG.pdf
North Atlantic Fisheries Organisation. 2010. Report of the Working Group on Greenland Halibut Management
Strategy Evaluation (WGMSE). 16-17 September. Halifax, Nova Scotia, Canada. 64 pp. Available athttps://www.nafo.int/Portals/0/PDFs/fc/2010/fcdoc10-30.pdf
Northeast Fisheries Science Center. 2011. 52nd Northeast Regional Stock Assessment Workshop (52nd SAW)
Assessment Report. US Dept Commer, Northeast Fish Sci Cent Ref Doc. 11-17. 967 pp. Available athttps://www.nefsc.noaa.gov/saw/saw52/crd1117.pdf .

7

ADJUNCT 2
Enhancement of the Detection of the Effects of Environmental Factors on Whale Dynamics given the
Availability of Catch–at-Age Data

Introduction
This Appendix intends to provide an illustration of how the availability of catch-at-age data may lead to
improved estimation of the effect of an environmental factor (or factors in combination) on whale population
dynamics. Specifically the magnitude (G) of the trend in an enhancement of recruitment success is estimatedwithout and with the availability of catch-at-age data, and estimation performance contrasted in terms of bias,
variance and root mean square error.
The situation modelled is loosely based on the O stock of North Pacific common minke whales, and isdeveloped from the model of Kitakado and Maeda (2016), and as refined further in Adjunct 3. Details of themethods applied are set out in Appendix A. The data available to the estimator are the results of six-yearlysightings surveys of 1+ abundance, and annual catch-at-age information with an effective (i.e. “independent”)
sample size of either 0 or 80.
Estimation performance for the environmental effect (G) parameter is compared after 20 and after 50 years forthe two different sample sizes for catch-at-age data.
Results and discussion
Table 1 summarises the results for 20, and 50 year projections given 0 and 803 age samples each year, while
Figures 1 plots the results.
The bottom row of Figure 1 in particular makes visually evident that there is an improvement in precision of theestimated trajectory of female births when the age data are also available to the estimator.
The extent of this improvement is best quantified by the statistics in Table 1 which relate to estimation
performance for G. After 20 years there is appreciable negative bias in the absence of age data and variances
are large, but the RMSE is appreciably less when age data are available. After 50 years bias and variance aresubstantially reduced, and the RMSE (which still remains less if age data are provided) is reduced in that lastcase to a level where a result is obtained which is almost statistically significant at the 5% level.
References
Kitakado, T and Maeda H. 2016. Fitting to catch-at-age data for North Pacific common minke whales in the
Pacific side of Japan. Paper SC/F16/JR43 presented to the Expert Panel of the final review on thewestern North Pacific Japanese Special Permit programme (JARPN II) (unpublished) 12pp.

3

As in Adjunct 3, the actual annual catch here is 107, but after allowing for over-dispersion, the effective“independent” sample size is 80.

8Table 1: Mean, standard deviation, CV and root mean square error (RMSE) for the estimated environmental
effect on recruitment parameter G (the true value of G is 0.005) after periods of 20 and 50 years, and given
either an effective n=0 or n=80 age samples each year
20yrs
mean
stdev
CV
RMSE

n=0
0.013930.03106
2.229930.03217n=80
0.00634
0.011271.777440.0112950yrs
n=00.006620.00387
0.583450.00417n=80
0.00598
0.00251
0.41898
0.00268

Figure 1: Top row: Medians for “true” and estimated total numbers and female births for sample sizes of 0 and
80 after 50 years. Second row: Medians for “true” and estimated female births for a sample size of 0 (LHS) and80 (RHS), estimated after 20 and 50 years. Third row: Estimated 95%iles, some individual trajectories estimatedand “true” female births for a sample size of 0 (LHS) and 80 (RHS) after 50 years.

9Appendix A – Methodology

The text following sets out the equations and other general specifications of the SCAA estimation approach
followed by details of the contributions to the log-likelihood function from the different sources of data
available. Quasi-Newton minimization is then applied to minimize the total negative log-likelihood function toestimate parameter values (the package AD Model BuilderTM (Fournier et al. 2012) is used for this purpose).
A.1. Population dynamics
A.1.1 Numbers-at-age
The resource dynamics are modelled by the following set of population dynamics equations:

(A1)

where
is the number of whales of gender g and age a at the start of year y,is the catch (in number) of whales of gender g and age a during year y,is the number of calves born at the start of year y,is the survival rate

whereis the instantaneous rate of natural mortality (assumed to be

independent of gender),is the maximum age (treated as a plus-group).

A.1.2. Births
Density-dependence is assumed to act on the female component of the mature population.
(A2)
where
is the average number of births (of both genders) per year for a mature female in the pristine
population,is the resilience parameter,is the degree of compensation,is the number of mature females at the start of year y,is the earliest age-at-first parturition;is the proportion of females of age a which have reached the age at first parturition (ogive withparameters given in Table A.1), andis the number of mature females in the pristine population.

10A.1.3. Total catch and catches-at-age
The catch-at-age is given by:
(A3)
where
is the catch-at-age, i.e. the number of animal of gender g and age a caught during year y,is the commercial selectivity of an animal of gender g and age a for year y; when

, the age-class a is said to be fully selected, andis the proportion of a fully selected age class that is caught.

A.1.4. Initial conditions
For the first year (y0) considered in the model, the numbers-at-age are taken to be at unexploited equilibrium,
i.e.:

(A4)

Input values for the model parameters and data were selected to give a typical population trajectory, which is at
about 50% of carrying capacity in the year the projections start (see Tables A1 and A2).
A.2. Projections
For each simulation i, the population is projected forward using equation A1 and a constant catch of 107 animals
per year.
Future recruitments include residuals and an environmental effect G = 0.005 which reflects a 0.5% increase perannum in recruitment (density dependent effects aside):
(A5)
with
Future observed abundance indices are computed as:
(A6)
with
Future catch-at-age data are generated under the assumption of a multinomial error distribution:
(A7)
where

11

is the observed number of whale of age a and gender g caught in year y for simulation i,is the ageing error matrix (Table A3), andis the age readability at age a for gender g (Table A4).
The standardised residuals are computed as:
(A8)
with
(A9)

A.3. The likelihood function
The model is fitted to projected estimates of total (1+) numbers (from surveys every six years, starting in thefirst year of the projection) and annual catch-at-age data to estimate model parameters. Contributions by each ofthese to the negative of the log-likelihood () are as follows.

A.3.1 Estimates of total (1+) numbers

(A10)
with
(A11)
A.3.2. Commercial catches-at-age
The contribution of the catch-at-age data to the negative of the log-likelihood function under the assumption of amultinomial error distribution is given by:(A12)
A.3.3. Female births
The following penalty added to the negative log-likelihood given the variability about the stock-recruitment
relationship:(A13)

The model assumes an unexploited equilibrium age structure in the starting year (1930). The estimableparameters of the model are K, the environmental effect G, and the annual recruitment residuals .
References
Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., and Sibert, J.
2012. AD Model Builder: using automatic differentiation for statistical inference of highly
parameterized complex nonlinear models. Optim. Methods Softw. 27:233-249.

12

Table A1: Model parameter values assumed. Note that maturity and selectivity are logistic, with am referring to
the earliest age at which first parturition can occur.

13

Table A2: Historical male and female catches assumed.

14

Table A3: Ageing error matrix.

15

Table A4: Age readability proportion for males and females.

16

ADJUNCT 3
Analyses underlying choice of sample size for Primary Objective I (common minke whale) for the Pacificside of Japan (sub-areas 7-9)

Introduction
This Adjunct provides the details of an illustrative example of the extent of improvement achievable for theassessment of the dynamics of the O stock of western North Pacific common minke whales in relation to thesize of the sample taken to provide age data.
The approach followed is founded on the SCAA methodology applied to this stock by Kitakado and Maeda(2016), which is used to generate future data in a simulation testing context. The intent is to illustrate how well,using the SCAA methodology to analyse the future data generated, it is possible to detect changes in recruitment(strictly in the number of recruits per adult female) – specifically whether the sample sizes proposed do secure ameaningful improvement in this detectability.
Data and Methodology
The data used for these analyses are set out in Appendix A. Appendix B provides details of the SCAA
assessment methodology. Note that this remains as in Kitakado and Maeda (2016), and has not been extended toincorporate some of the suggestions made by the 2016 JARPNII review panel, such as allowing for domeshaped selectivity. The reason is that those extensions are not of particular pertinence to the issue under
examination in the illustrative exercise here, and are considered to better await subsequent work when the NP
minke RMP trials are re-conditioned on a basis that includes the use of age data, when they will likely also
estimate rather than pre-fix natural mortality (note that estimates of natural mortality at age for larger ages andthe extent of doming in the selectivity function are confounded).
Appendix C details how the population model of Appendix B is used to generate the future data required to test
how well the SCAA approach can estimate future recruitments. These data comprise annual catches at age aswell as six-yearly estimates of population abundance. Note that the effects of ageing error are incorporated inboth the assessment (Appendix B) and in the projections (Appendix C). The age data are generated using a
multinomial distribution, but analysis of existing data suggests some over-dispersion. Appendix D explains thisand how it is taken into account.
Results
Results are presented to show first the dependence on (aged) sample size of the detectability of a 30% decrease
in recruits per adult female. Changes of such a magnitude over a relatively short period are evident from the
SCAA assessments of Antarctic minke whales (GOJ, 2015). For the scenario examined, this change is assumed
to take place 10 years into the projection period (corresponding to 2022).
The second scenario considered includes instead a 30% increase in recruits per adult female, taking place 10
years into the projection period.
The third scenario considered is based on the recruitment variability evident for Antarctic minke (stocks I and P)
as estimated in SCAA results for Antarctic minke whales reported to the 2016 annual meeting of the ScientificCommittee. The 1970-2010 vector of moving averages for recruitment variability (renormalized so that the1970 value is 1, see Table 1 for I and P stocks) is used to project recruitment forward from 2011 (using the 1971
value) onwards, with the 2051 value taken to apply to all years from 2052 onwards. A three-year moving
average is used to eliminate some of the estimation error around the real underlying trend; values prior to 1970
are not used as they reflect more model assumptions than being informed by the actual age data. Equation C8(see Appendix C) for future births is modified:

(1)

17

Results are shown for estimation of O-stock trajectories in terms of annual female births. They compare acrossdifferent “multinomial” sample sizes (n) for the acquisition of age information (see Appendix D for how these“effective” sizes are related to actual sample sizes when allowance is made for over-dispersion). Figure 1 reports
results for a scenario which considers a 30% drop in recruits per adult female after the 10 th year of the
projections for the A01_1 RMP trial (IWC, 2014) which sets MSYR(mature) equal to 1%. The results areshown for estimation after 20 and after 50 years, and include both medians and worm plots (for ten
realisations/individual trajectories, and also showing the 90% probability envelopes shaded). Figure 2 shows
similar results for this same situation except that recruits per adult female increase instead of decreasing by
30%. Figure 3 shows such results for the A01_1 trial for P stock MSYR(mature) 1% equivalent recruitment
variations.
These illustrative results show clearly that the recruitment change is not detected in the absence of age data.
Furthermore detectability improves with both an increasing age sample size and a longer period of data
availability, and is meaningful for the sample size (n=80) proposed (even after 20 years). Note that after
adjustment for over-dispersion as indicated in Appendix D, this effective sample size of n=80 is increased to an
actual size of 107.
References
Government of Japan, 2015. Proponents additional responses to the Report of the Expert Panel to review the
proposal for NEWREP-A. Paper SC/66a/SP8 presented to the IWC Scientific Committee meeting, May 2015
(unpublished) 37pp.
International Whaling Commission. 2014. Report of the Scientific Committee. Annex D1. Report of theWorking Group on the Implementation Review for Western North Pacific Common Minke Whales. J. Cetacean
Res. Manage. (Suppl.) 15: 112-88.
Kitakado, T and Maeda H. 2016. Fitting to catch-at-age data for North Pacific common minke whales in the
Pacific side of Japan. Paper SC/F16/JR43 presented to the Expert Panel of the final review on the western North
Pacific Japanese Special Permit programme (JARPN II) (unpublished) 12pp.

18

Table 1: Moving averages of recruitment variability (renormalized so that the values for 1970 for Antarctic
minke and for 2011 for the assumed projected values for the North Pacific O stock of minke whales are 1). The
Antarctic values correspond to those reported for the I and P stocks of Antarctic minke whales for an MSYR of
1% in analyses presented by Kitakado to the 2016 meeting of the IWC Scientific Committee. The 1970-2010
values from the Antarctic are used in the projections for the 2011-2051 period for the North Pacific, with values
being kept constant after 2051.

19

Figure 1: Estimates of female births for scenario A01_1, with MSYR 1% recruitments, with a 30% drop in(per capita) recruitment after 10 years. The left side plots compare medians of estimates after 20 and after 50
years with the true values. The right side plots show worms (individual realizations) after 50 years together with
90% probability envelopes (shaded). The rows reflect different effective (i.e. “independent”) annual sample sizes
for age, ranging from n=0 to n=120.

20

Figure 2: Estimates of female births for scenario A01_1, with MSYR 1% recruitments, with a 30% increasein (per capita) recruitment after 10 years. The left side plots compare medians of estimates after 20 and after
50 years with the true values. The right side plots show worms (individual realizations) after 50 years together
with 90% probability envelopes (shaded). The rows reflect different effective (i.e. “independent”) annual sample
sizes for age, ranging from n=0 to n=120.

21

Figure 3: Estimates of female births for scenario A01_1, for P stock MSYR 1% equivalent recruitment
variations. The left side plots compare medians of estimates after 20 and after 50 years with the true values.
The right side plots show worms (individual realizations) after 50 years together with 90% probability envelopes(shaded). The rows reflect different effective (i.e. “independent”) annual sample sizes for age, ranging from n=0
to n=120.

22

Appendix A - The data

The catches assumed by regions/stocks for males and females separately are given in Table A1 (Cherry Allison,pers. commn). These catches are median outputs from trials A01_1/4 which are detailed in IWC (2014). For the
one stock hypotheses, the catches for males and females have been split by region corresponding to OW and OE(see details given below), assuming the same OW:OE proportions as those in the corresponding C01_1 andC01_4 trials.
The numbers assumed for mature females in 2000 are provided in Table A2. They correspond to deterministic
values for the associated trials, kindly provided by Cherry Allison.
Table A3 gives the males and females catches-at-age from JARPN surveys for the regions corresponding to OW
and OE. Catches in sub-areas 8, 9 and 7E have been assigned to region/stock OE. Catches in sub-areas 11, 7CN
and 7CS have been assigned to region/stock OW. Catches in sub-area 7WR have been assumed to belong toregion/stock OE if taken east of 145E and OW otherwise.
Table A4 lists the life history parameters used (IWC, 2014).
The ageing error matrix is given in Table A5 and is taken to be the same for males and females, across regions.
The sex-specific age readability vectors are listed in Table A6. The assumed proportion of the total sample ofmales and females in each region, based on averages over the 2000-2010 period for JARPN and JARPNII isshown in Table A7.

23

Table A1: Historical male and female minke catches assumed (see text above for source).

24

Table A2: Number of mature females in 2000 (from NP minke ISTs – see text above - Cherry Allisson, pers.
commn). Only the A01_1 scenario is considered here.

25

Table A3: Catch-at-age data from JARPN and JARPN II surveys for regions corresponding to OW and OE.

26

Table A4: Life history parameter values (as defined for the ISTs detailed in IWC (2014)). The maturity ogive islogistic, with am the earliest age at which first parturition can occur.

27

Table A5: Ageing error matrix.

28

Table A6: Age readability proportion for males and females. These values were obtained by fitting logistic
curves to the data available.

Table A7: Assumed proportion of the total sample of males and females in each region, based on averages overthe 2000-2010 period for JARPN and JARPNII.

29

Appendix B - The Statistical Catch-at-Age Model

The text following sets out the equations and other general specifications of the SCAA followed by details of
the contributions to the log-likelihood function from the different sources of data available. Quasi-Newton
minimization is then applied to minimize the total negative log-likelihood function to estimate parameter values(the package AD Model BuilderTM (Fournier et al., 2012) is used for this purpose).

B.1. Population dynamics

B.1.1 NUMBERS-AT-AGE

The resource dynamics are modelled by the following set of population dynamics equations:

(B1)

where
is the number of animal of gender g and age a at the start of year y,is the catch (in number) of animal of gender g and age a during year y,is the number of calves born to females at the start of year y,is the survival ratewhere
independent of gender),is the instantaneous rate of natural mortality (assumed to beis the maximum age (treated as a plus-group).

B.1.2. BIRTHS

Density-dependence is assumed to act on the female component of the mature population.
(B2)

where
is the average number of births (of both genders) per year for a mature female in the pristine
population,is the resilience parameter (see Table A4),
is the degree of compensation (see Table A4),
is the number of mature' females at the start of year y,30

is the earliest age-at-first parturition (see Table A4);
is the proportion of mature female of age a,is the number of mature females in the pristine population, andwith

B.1.3. TOTAL CATCH AND CATCHES-AT-AGE

The catch-at-age is given by:

(B3)

where
is the catch-at-age, i.e. the number of animal of gender g and age a caught during year y in region k(where k refers to inshore/offshore),is the commercial selectivity of an animal of gender g and age a for year y in region k; when
the age-class a is said to be fully selected, and,is the proportion of a fully selected age class that is caught in region k.

B.1.4. INITIAL CONDITIONS

For the first year (y0) considered in the model (here 1930), the numbers-at-age are taken to be at unexploitedequilibrium, i.e.:(B4)

B.2. The likelihood function

The model is fitted to estimates of mature female numbers and catch-at-age data to estimate model parameters.
Contributions by each of these to the negative of the (penalised) log-likelihood () are as follows.

Mature female numbers

(B5)

31

with
(B6)

where
is the estimate of mature female numbers in year y, andCommercial catches-at-age
The contribution of the catch-at-age data to the negative of the log-likelihood function under the assumption of amultinomial error distribution is given by:

(B7)

where
is the observed number of whale of age a and gender g caught in year y in region k,is the model-predicted number of whale of age a and gender g caught in year yin region k,
where
(B8)

with
being the ageing error matrix (Table A5), andbeing the age readability at age a for gender g (Table A6).

The standardised residuals are computed as:

32

(B9)

with

(B10)

Female births (recruitment) residuals are defined by:
(B11)

where

from

with

with the following penalty added to the negative log-likelihood:(B12)

The standard deviations for total numbers are computed as follows, taking account of the estimation bias:(B13)with the bias computed as:(B14)

and similarly for the female births.
To allow for a better fit, carrying capacity K is allowed to change (by a limited amount) every 10 projected
years, starting in 2012, but staying constant during each of these 10 year periods:33

(B15)

with the following penalty added to the negative log-likelihood:
(B16)

with

.

Thus, aside from selectivity-related parameters, the estimable parameters of the model are K, and the 10-yearly
together with the annual recruitment residuals .

B.3. Harvesting selectivity

Fishing selectivities-at-age in each region k are estimated using a logistic form:(B17)
Pre-1988, the selectivities are taken to be the same for males and females, with the parameters fixed:   4
and   1.2 (i.e. as for the trials detailed in IWC (2014)).
Post-1988, the selectivities are estimated separately for males and females. Furthermore,
female selectivity, so that:is estimated for the(B18)

Reference
Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., and Sibert, J.
2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized
complex nonlinear models. Optim. Methods Softw. 27:233-249.

34

Appendix C – Projection methodology

Projections into the future and their evaluation are developed using the following steps.

Step 1: Begin-year numbers-at-age
The components of the numbers-at-age vector for each gender g and at the start of 2012 (from the MLE of an assessment.

) are obtainedError is included for all ages, i.e.:
(C1)
where
is taken to be 0.2 (independent of age).

Step 2: Catch
These numbers-at-age are projected one year forward at a time given a catch for the year concerned.

This requires specification of how the catch is disaggregated by gender and age and region to obtainhow future births are generated.

, andStep 3: Catch-at-age by region, gender and age
Catch by region:
A 60:40 ratio is assumed for the future catches in the inshore:offshore regions.

Catch by gender:
The male/female fishing mortality ratio is taken to stay constant at the 2007-2011 average estimated in theassessment:(C2)
so that:
(C3)
and
(C4)
The catch by gender is computed by:(C5)

Catch by age
is obtained by assuming that the commercial selectivity of an animal of gender g and age a for year y (stays constant in the future as estimated in the assessment.
(C6)

35

)

The numbers-at-age can then be computed for the beginning of the following year (y+1):(C7)

Step 4: Births
Future births are obtained assuming a density-dependence acting on the female component of the mature
population.
(C8)

Step 5: Generate data
The information obtained in Steps 1 to 4 is used to generate NEWREP-NP catch-at-age data. These data are
generated assuming the same multinomial error structure as in the past. The multinomial parameters are theprobabilities for each age and the sample size. The probabilities are the expected proportions-at-age for genderg:(C9)while the sample size for the corresponding gender is a fixed proportion of the total sample size:
(C10)
with
given in Table A7.
Note that because the purpose of the exercise is to compare estimation performance for different sample sizes
for age data, it is desirable that the true numbers-at-age trajectories are the same though these sample sizes
differ. This has been done by computing the dynamics using the largest sample size considered (160 in thiscase), and then scaling down the age-readability vectors to reflect the actual sample size.
In addition, survey estimates of abundance are generated every six years, commencing in year 2012, as follows:
(C11)
with

.

Step 6: Conduct updated assessment using generated data
The updated assessments follow the procedures set out in Appendix B, incorporating all those historical data in. For the generated data, the catch-at-age data is included as for the historical data. The generated survey
estimates of abundance are included using equation B5, with
, i.e. the assessment assumes the samevalue as used in generating these data.

36

Appendix D – Over-dispersion in catch-at-age data

Over-dispersion in the catch-at-age data has been estimated from the fit of the baseline population model to the
A01_1 scenario, using the following equation:(D1)

where the ratio of summations over years rather than the average of yearly ratios was used in the interests ofmore robust estimation in the face of differing annual sample sizes.
The result of the calculation yielded D = 1.34, i.e. an increase of about one third.
The original intention was to generate future catch-at-age data from an over-dispersed multinomial distribution
with this value of 1.34 for the over-dispersion parameter. However for the small samples sizes typical for mostof the ages considered here, standard Dirichlet–multinomial generation procedures exhibit large small sample
bias, precluding their use.
In these circumstances the assumption has been made that effective sample size scales inversely with variance.
Hence, given that the value D = 1.34 reflects an increase of about one third, for an actual intended sample sizeof N, the sample size used when generating catch-at-age data from a multinomial has been set at n =0.75N.

37

ADJUNCT 4
The selection of sample size North of Hokkaido (sub area 11)
There is limited information on mixing proportion of J stock in sub area 11 (see Annex 7). An examination of
the required sample size to estimate the annual mixing proportion in this sub area with sufficient precision (e.g.
SE (p) is less than 0.1).
In the formula below p is the proportion of J stock in the sub-area and n is the number of the samples in the subarea. Standard error of p, SE( p̂ ) is:

SE ( pˆ ) pˆ 1  pˆ n(1)Considering overdispersionφ(>1), equation (1) becomes:

SE ( pˆ ) pˆ 1  pˆ n(1)’

SE( p̂ ) is a maximum when p=0.5 for fixed n. Therefore for this calculation p=0.5 is assumed, without loss ofgenerality. It is desirable to obtain the proportion of the J stock in sub-area 11 with sufficient precision (e.g.
SE( p̂ ) is less than 0.1). To fulfil this condition:

0.5 

 0.1
n
n  25

(2)

Considering that the ratio of unassigned samples is 9% in sub-areas 7CS and 7CN (data from the JARPNII
coastal component), the required sample size should satisfy the following condition:n25
(1  0.09)(3)

The overdispersion parameter was estimated by using the samples in sub-area 7CN, assuming a quasi-binomial
error in conducting Generalized Linear Model GLM):p

exp  a1  a 2 y 
1  exp  a1  a 2 y 

(4)where a1 and a2 are coefficients to be estimated and y indicates year. The overdispersion parameterφwas
estimated as 1.689. Substituting the formula (3) for the estimated parameter the resulting sample size is 47.
It should be noted that this estimate is preliminary, and only applies for the first six years of NEWREP-NP.
More detailed estimates of sample sizes for the objective of studying yearly trend in the J stock proportion will
be made once new data are accumulated after the first six surveys.

38

ANNEX 16
Estimates of sample size for Primary Objective II (sei whale)

Introduction
This annex introduces an approach to estimate the proposed sample size for the North Pacific sei whales to meetthe Primary Objectives II, especially the Secondary Objective II (ii). The approach followed is based on the ageand sex-structured model applied to this stock for conditioning and generating future data in a simulation. The
target is to estimate the natural mortality rate, M, by using the SCAA methodology.
Materials and methods
Data for conditioning
The data used for the conditioning are as follows:

i)
ii)
iii)

Catch series since 1906 (aggregated over ages and sexes) (Figure 1).
Sex-specific catch-at-age data for commercial period (1966-1973) and those from JARPNII (20022013) (Figures 2 and 3).
An abundance estimate of the sei whales, 34,718 (CV=21.4%) in the whole North Pacific area
(Hakamada and Matsuoka, 2015; 2016). Note that this estimate adds the contributions from the
IWC-POWER and JARPNII cruises which covered non-overlapping areas

Model assumed for conditioning
The population dynamics assumed for the conditioning is the same as the model shown in Annex 14 except for
the recruitment as   P  z Rt  f Pt exp  A 1   t ,1     K  

 
F

The plus group age is set at m=40, and in the maturity ogive, age at 50% maturity is fixed at 7.5 with scaleparameter 1.2. The natural mortality is assumed to be age-independent as M=0.04 and 0.05 (/year) (the reasonsfor these choices are explained below), and the MSYR(1+) is set at 1 and 2.5%. These values are used not only
for conditioning but also for generating future data in the simulation context to assess the estimation
performance of the natural mortality coefficient.

For the estimation process, given the single abundance estimate available, a procedure like ‘Hitter’ was applied,which means the standard deviation of abundance estimate was intentionally set at a tiny value (here at 0.01)
while naive multinomial distributions were assumed for the catch-at-age data. Unknown parameters to be
estimated in the model fitting process) are the carrying capacity, sex- and fleet- (same as period-) specificselectivity parameters given the values of M and as well as MSYR (equivalently given A). The fecundity issolved internally assuming that population in the start year 1906 is at equilibrium.

39

Conditioning gave rise to some problems because the historical catch-at-age data for the commercial period
show substantial variability and are in fact made available only as ages rounded to odd integers. The likelihoods
obtained did, however, indicate a preference for M values close to the 0.04 to 0.05 range.

Model assumed for simulation
Based on the conditioned models, projections were conducted to generate future abundance estimates and catchat-age data. Given the somewhat questionable nature of the historical age data from commercial whaling, it was
decided not to use these when fitting the model given additional data generated in the future; furthermore
commercial and research selectivities (the same for the past and the future) were fixed at their values estimatedfor the MSYR/M scenario concerned. For 12 years research period, it was assumed that an abundance estimate
is available twice though not for the whole area of the North Pacific but only the survey area covered under
NEWREP-NP. These abundance estimates are subject to the process error due to inter-annual variations inspatial distribution, and therefore it was assumed that the abundance estimates generated when inflated to thewhole area for use in the simulations have a larger CV (30%) than CV=21.4% for the actual survey to takeadditional variance into consideration.

In the projection and generation of future data, log-normal deviations are incorporated into the recruitment
although these recruitment deviations were turned off in the estimation process. The projection starts from 2014
because the model was conditioned data up to 2013. In the 3-year gap, the actual catch was allocated to age
composition using estimated selectivity and numbers-at-age. For future catch-at-age data, multinomial
distributions were used without assuming any overdispersion and age-reading error. Age-readability was
assumed to be 70% across all the ages based on a coarse analysis. The various annual sample sizes for the 12
years of the research program generated for the evaluation were 40, 60, 80, 100, 120 and 140. For each sample
size, data generation and estimation were repeated 100 times (i.e. n=100 for the measures defined below).
Estimation assumed the true selectivities, so that only carrying capacity K and natural mortality M were
estimated estimated. The existing JARPNII age data were not included in the likelihood for these fits because of‘double usage’ concerns since they had been used to fix the research selectivity; including them would not havea large effect on results as they total only 100, which is small compared to the sample numbers to beaccumulated over the research period.

Performance measures
The parameter of interest is the natural mortality (M), and therefore the following three measures are used forevaluation of estimation performance by sample size.

RMSE 

CV 





2
1 n ˆ
 Mi  M
n i 1





2
1 n ˆ
Mi  M / Mn i 11 n
Relative Bias  100   Mˆ i  M  / M
 n i 1
40

Results and conclusion

Figure 4 shows the performance measures for the four scenarios (true M.MSYR combinations) considered.
Robust results across these scenarios are that for an annual sample size n of 100 or above, bias reduces to close
to zero, and RMSE stabilises at about 0.005. Figure 5 illustrates how the variance of the distribution of M
estimates narrows considerably as the sample size is increased from 40 to 100.
This value makes no allowance for possible over-dispersion in the age data, and the sample sizes available are
too small to estimate this reliably. Consequently the assumption has been made that this is the same as for minke
whales, corresponding to a need to increase the sample size by a multiplicative factor of 1.34 (see Appendix D
of Adjunct 3 of Annex 11).
Consequently the proposed annual sample size for sei whales is 134.

References
Hakamada, T. and Matsuoka, K. 2015. Abundance estimate for sei whales in the North Pacific based on sighting
data obtained during IWC-POWER surveys in 2010-2012. Paper SC/66a/IA12 presented to IWC ScientificCommittee, May 2015 (unpublished). 11pp.
Hakamada, T. and Matsuoka, K. 2016. The number of western North Pacific common minke, Bryde’s and sei
whales distributed in JARPNII Offshore survey area. Paper SC/F16/JR12 presented to the Expert Panel of the
final review on the western North Pacific Japanese Special Permit programme (JARPN II),Tokyo, February 2016 (unpublished). 13pp.

41

Figure 1. Catch series for the North Pacific sei whales.

Figure 2. Catch-at-age data for male (left) and female (right) in commercial period (1966-1975)

42

Figure 3. Catch-at-age data for male (left) and female (right) in JARPN-II period (2002-2013).

43

Figure 4. Summary of estimation performance for the true values of M at 0.04 and 0.05 under MSYR(1+)=1.0
and 2.5%.

Figure 5. Histogram of estimates of M for the sample sizes 40 and 100 under M= 0.04 and MSYR= 2.5%

44

SECTION 4
Assessment of Potential Effect of Catches4
Primary Objective I (common minke whale)
Three stock structure hypotheses were used in that Implementation Review (IWC, 2013b) (see theIntroduction section of the revised proposal for more details of these hypotheses). In essence:

Hypothesis A

J and O stocks

Hypothesis B

Y, J and O stocks

Hypothesis C

Y, Jw, Je, Ow and Oe stocks

The baseline trials for those hypotheses developed in that Implementation Review have been used here
to assess the effect of catches, except that:a) Hypothesis B has not been considered, as the hypothesized Y stock to the west of Korea is not
impacted by the catches under consideration.
b) Those trials (ISTs) considered the lowest plausible MSYR value to be MSYR(mat) = 1%. The
Scientific Committee has subsequently agreed that this minimum be increased to MSYR(1+) = 1%
[IWC, 2014b], so that the deterministic versions of the trials in question have been reconditioned
with MSYR(1+) values of 1%, 2%, 3% and 4% (only the value of MSYR was changed in thesereconditionings).

The constant future annual research catches considered when projecting under the proposed annual
take of 170 minke whales is divided amongst sub areas as set out in Table 4.1.1, which corresponds tothe temporal and spatial allocation proposed.

Table 4.1.1: Distribution of future J and O whale research catches amongst sub areas. Catches takeplace in the months of April-October

Sub area
Annual catch

7CS
40

7CN
40

7WR
5

7E
5

8
10

9
23

11
47

Projections under these catches for MSYR(1+) values of 1% and 2% are shown in Figure 4.1.1 for
Hypothesis A for the J and O stocks, and in Figure 4.1.2 for Hypothesis C for the Jw, Je, Ow and Oe
stocks. Note that these projections assume that current levels of bycatch continue unchanged. To ease4

To aid the reader, the text below contains some repetition of text in the main body of the program proposal.

45

understanding, projections are also shown for the case of these bycatches only, with no research
catch5.

Figure 4.1.1. Projections under the proposed catches for MSYR(1+) values of 1% and 2% for
Hypothesis A for the J and O stocks. Depletion refers to the mature female component. Note that thezero catch results refer to the situation of no commercial or research catch, but bycatch continuing as
in the immediate past.

5

The projections here have used the IWC code which allows only for a fixed catch each year following the lastyear of the assessment, i.e. projections here start in 2013. Thus the proposed scientific catches have beentaken to apply starting in 2013, rather than in 2017. Strictly the actual catches made over 2013-2016 should be
input, but the resultant differences to the projections will be very small and of no consequence to the key
conclusions..

46

Figure 4.1.2. Projections under the proposed catches for MSYR(1+) values of 1% and 2% for
Hypothesis C for the Jw, Je, Ow and Oe stocks. Depletion refers to the mature female component.
Note that the zero catch results refer to the situation of no commercial or research catch, but bycatch
continuing as in the immediate past.
47

For MSYR(1+) = 2%, all stocks show increases and/or are well above 54% of their pre-exploitationlevels under the research catches proposed, so there are no population conservation concerns.
For MSYR(1+) = 1%, under Hypothesis A the J stock is currently less than 54% of its preexploitation level and is projected to continue to decline, while under Hypothesis C the same applies
for the Jw stock (though this is a consequence of the bycatches only, as no research take from subareas where this stock is present is planned), and the Ow stock, currently at 70.2% of its preexploitation level, decreases slowly to reach 66.3% by 2066. However, while these instances might beconsidered by some to be population conservation concerns, the proponents consider that issue to be
negligible, as recent information/analyses have shown the associated stock structure/MSYR
combinations to be clearly implausible, for the reasons set out below.
In summary, the results provided therefore show that the research catches proposed will not adversely
impact the stocks, so that no population conservation concern arises.

The assumption of an MSYR(1+) value for 1% for the J stock in Hypothesis A
The conditioning of the North Pacific common minke whale ISTs includes a component in theobjective function which secures trends in whale abundance that are consistent with by-catch per unit
effort in fixed set nets, including those off Japan (see equation F.6 in IWC 2014c).
However this conditioning does not take account of further information that is available on the J:O
split of these bycatches which indicates an increasing proportion of J whales, contrary to what might
be expected if the J stock was heavily depleted and continuing to decline under the current bycatches
as indicated for MSYR(1+) = 1% in Figure 4.1.1 above. Figure 4.1.3 compares this historicalobserved trend with the annual values for the proportion of J whales in the J+O total for different
values of MSYR(1+) as predicted, and is suggestive that this further J:O bycatch ratio information
may be able to discriminate amongst different MSYR(1+) values. Note that the explanation for the
recent increase in the J stock proportion in the overall population as MSYR increases is that the O
stock is hardly depleted so that its numbers hardly change, whereas the J stock has been more
substantially reduced in the past, and has recently been changing at a fairly large rate that increases
with MSYR.
Figure 4.1.4 compares the estimate of annual trend provided by log-linear regression of the J
proportion of the bycatch (with the associated 95% CI) with the point estimates of the trends from the
Hypothesis A model abundances for different MSYR(1+) values. This suggests an MSYR(1+) valueof 2.8% with a lower 95% confidence limit of 1.6%.
Deterministically such a log-linear relationship is the more justified for the J:O bycatch ratio
compared to the J proportion, but precision decreases because of the greater variance in ratio data.
Such an analysis does, however, suggest a larger value for the lower 95% confidence limit for
MSYR(1+) of 2.8%.
These bycatch data are thus strongly suggestive of a MSYR(1+) value of 2% or more, for which theresults discussed above indicate no conservation concern for the J stock under the research catches
proposed.

48

Figure 4.1.3. Comparison of historical observed trend in the proportion of bycatch of J whales in the
J+O total with the trend in the proportion of J whales in the J+O total for different values ofMSYR(1+) as predicted under Hypothesis A.

Figure 4.1.4. Comparison of the estimate of annual trend provided by log-linear regression of theobserved bycatch (with the associated 95% CI) with the point estimates of the trends from the
Hypothesis A model abundances for different MSYR(1+) values.
49

A more formal investigation is possible through noting that the bycatch model used for the ISTs (IWC
2014c) indicates that the expected value of the J stock bycatch proportion in each (pertinent) sub areafor each year is equal to the corresponding proportion (appropriately averaged over months) of thenumber of whales in that sub area each year, specifically:

E[CBk ,,ts,m ]  Ak Ptk,m,s Et ,(4.1)

where E[CBk ,,ts,m ] and Ptk,m,s are the expected bycatch and the number of whales in year t and month mfor subarea k and stock s (J or O), respectively (these two quantities depends on the value ofMSYR1+); Ak is a sub-area effect; and Et is an effort in year t.
Assuming catches are Poisson distributed, so that these J stock proportions are binomially distributed,
leads to the following negative log likelihood as a function of the value of MSYR(1+):

NLL( MSYR1 )   C log 
k ,J
B ,t

k ,J
 CBk ,,Ot log  tk ,O )  ,
t


(4.2)

t ,k

where

 E[C ]  A P E
P E[C ]  A P E  P
k ,s
B ,t ,mtk ,s

k

m

k ,s
t ,m

t

m

m

k ,s
B ,t ,m

s

k ,s
t ,m

m

k

s

m

k ,s
t ,m


k ,s

t ,m

s

m

Ptk ,sPk ,s( s  J/O)

(4.3)

t

s

and

Ptk ,s 

P

k ,s
t ,m

( s  J/O) .

m

Since Ptk,m,s depends on the value of MSYR(1+), the loglikelihood is a function of that value as
specifically shown in (4.2).
The associated computations indicate strong support for an MSYR(1+) value of 4% or more. However
there are systematic deviations from model predicted proportions for some sub areas, which are suchas preclude these results from being used to provide reliable confidence bounds. This indicates a needto refine the current bycatch model in the next Implementation Review for these minke whales. In themeantime, however, this result does provide qualitative support for the conclusion above based on asimpler approach regarding the value of MSYR(1+).
While in general terms there might be reservations about the assumption of CPUE being proportional
to abundance, these concerns are greatly reduced here because the effort in question relates to set nets
in fixed locations over time, and the analysis assumes only that the ratio of the stocks present in the
bycatches is given by the ratio of the populations of those stocks present in the sub area concerned.
50

The assumption of separate Ow/Oe and Jw/Je stocks for Hypothesis C
Tables 4.1.2 and 4.1.3 (duplicated from Taguchi et al., 2017) show the number of close-kin pairs
observed by sub area pairings for O and J whales. They also show which sub area pairings should andshould not evidence pairings in terms of the Hypothesis C mixing matrices for the assumed Ow/Oe
and Jw/Je stocks.

Table 4.1.2. The number of Parent-Offspring pairs of O stock whales within and between sub-areas (duplicate
from Taguchi et al., 2017). The blue color indicates those sub-area pairings that are not consistent with the
Hypothesis C mixing matrices for the assumed Ow/Oe stocks; the orange color indicates those sub-areas that are
not inconsistent with this hypothesis.

O stock

1E

2C

6E

7CN

1E

1

2C

1

7CS

7WR

7
5

7E

8

9

11

1

3

7

1

1

1

6

6E

1

7CN
7CS
7WR
7E

2

8

2

9

1

11

Table 4.1.3. The number of Parent-Offspring pairs of J stock whales within and between sub-areas (duplicate
from Taguchi et al., 2017). The blue color indicates those sub-area pairings that are not consistent with the
Hypothesis C mixing matrices for the assumed Jw/Je stocks; the orange color indicates those sub-areas that are
not inconsistent with this hypothesis.
J stock

2

6

7CN

7CS

2
6
7CN
7CS

2

4
1

2

51

Table 4.1.2 indicates 20 examples of close-kin pairs that are inconsistent with the Hypothesis C splitof O whales into Ow and Oe stocks. This constitutes compelling evidence that this split is NOT
supported by the data.
Table 4.1.3 indicates 2 examples of close-kin pairs that are inconsistent with the Hypothesis C split of
J whales into Jw and Je stocks. This is not quite as strong evidence as in the Ow-Oe case above, given
the lesser number of close-kin pairs observed which are inconsistent with the Hypothesis C
assumptions. Nevertheless this result, taken together with the view of geneticists that the genetic
evidence for a Jw-Je differentiation is in any case very weak (IWC 2013c), is sufficient to conclude
that this differentiation within J whales in Hypothesis C is not plausible.
In summary, taking account of the close-kin evidence now available, Hypothesis C may no longer beconsidered plausible.

Primary Objective II (sei whale)


Information on stock structure

The most comprehensive studies conducted so far with regard to the stock structure of the North
Pacific sei whales were those presented at the mid-term JARPNII Review workshop in 2009 (Kanda
et al. 2009) as well as those presented at more recent IWC SC meetings (Kanda et al. 2013). Thesestudies used microsatellite DNA loci and mtDNA markers to examine sei whales samples collectedfrom almost the entire range of North Pacific.
Kanda et al. (2009) analyzed genetic variation at 17 microsatellite DNA loci and 487bp of
mitochondrial DNA (mtDNA) control region sequences in the JARPNII samples (n=489) from 2002
to 2007 in the area between 143°E and 170°E as well as in the commercial whaling samples (n=301)
from 1972 and 1973 conducted in the area between 165°E and 139°W. The results indicated noevidence of significant genetic differences within as well as between the JARPNII and commercial
whaling samples. Both females and males showed the same pattern of the stock structure. Sequencing
and phylogenetic analysis of the mtDNA control region also showed no evidence of the genetic
heterogeneity in the JARPNII samples as well as no spatially or temporally unique phylogenetic
clusters.
Kanda et al. (2013) examined genetic variations at 14 microsatellite DNA loci in the North Pacific sei
whale using biopsy samples obtained from the IWC-POWER surveys that covered the 173°E - 172°W
area of the central North Pacific in 2010 (n=13), 170°W - 150°W area of the central North Pacific in
2011 (n=29), and 150°W - 135°W area of the eastern North Pacific in 2012 (n=35), and theseobtained data were analyzed with those in Kanda et al. (2009). This study allowed the authors to
examine temporal (40 years apart between the POWER and commercial whaling data) and spatial(143°E to 135°W area divided into western, central and eastern) genetic differences of the North
Pacific sei whales. Similar to Kanda et al. (2009), the results showed no evidence of the temporal
genetic differences between the recent POWER and past commercial whaling samples collected fromthe same area and no evidence of the spatial genetic differences among the western, central andeastern samples.
One drawback to these two studies was that there was no direct comparison among samples collected
at the same time of the year from the different areas over the North Pacific. Considering that sei
whales conduct seasonal migration from their breeding ground to feeding ground every year,
development of stock structure hypothesis should test the genetic differentiation in the samples
52

collected in the same year that eliminate temporal negative biases. If no genetic difference is found,
this would hardly suggest a strong possibility of multiple stocks in the area. Kanda et al. (2015a)
looked at genetic variation at the microsatellite DNA loci to analyze the JARPNII and POWER
samples collected from the same time of years in 2010, 2011 and 2012, respectively. Again the study
failed to demonstrate evidence of multiple stocks of sei whales in the North Pacific.
The in-depth assessment of North Pacific sei whale started at the 2015 IWC SC meeting. The IWC SC
agreed to proceed with two initial alternative stock structure hypotheses: i) a single stock in the entire
North Pacific as proposed by Kanda et al. (2015a;b), based on several pieces of evidence including
genetics; and ii) a five-stock hypothesis proposed in Mizroch (2015), based mainly on theinterpretation of mark-recapture data: Japan coastal; North Pacific pelagic; Aleutian Islands and Gulf
of Alaska; eastern North Pacific migratory; and Southern North American coastal stock (coastal
California) (IWC, 2015a). The IWC SC agreed that discriminating between these two hypotheses isdifficult in the absence of genetic data from the potentially extirpated stocks, and thus bothhypotheses are plausible (IWC, 2015a). The IWC SC agreed that the oceanic regions of the North
Pacific are composed of a single stock (IWC, 2015a).
At the 2016 IWC SC meeting the Committee agreed that the genetic and mark-recapture data
currently available are consistent with a single stock in the pelagic region of the North Pacific (IWC,
2016b).
Therefore the analyses conducted to evaluate the effect on the sei whale stocks of future NEWREPNP catches are based on the hypothesis of a single stock in the pelagic regions of the North Pacific towhich the catches to be made will be restricted.

The estimated abundance of the species/stocks, including methods used and an assessment of
uncertainty, with a note as to whether the estimates have previously been considered by theSC

Hakamada and Matsuoka (2015) estimated abundance estimate based on IWC-POWER data from the
2010-2012 surveys using the design based estimator and detection function with covariates following
the previous IWC SC recommendations. Considering the discussion at the IWC SC in 2015, Akaikeweighted average of the estimate of 29,632 (CV=0.242) was endorsed for use in the in-depth
assessment of the sei whales (IWC, 2016d).
Hakamada and Matsuoka (2016) estimated abundance of 5,086 (CV=0.378) in sub-areas 7, 8 and 9 inlate season based on the 2008 JARPNII sighting data using the design based estimator and detection
functions considering some covariates of detectability. The estimates were presented at the final
JARPNII review workshop and the review workshop recommended that exploration of methods toaccount for sampling differences between areas and years to obtain measures of short and long-term
variation and trends and estimates the extent of additional variance due to changes over time in spatial
distribution (IWC 2016c). The additional variance has not been estimated yet and this would causesome underestimation of variances of abundance estimates.
Since the areas covered by these two surveys do not overlap, the abundance estimates from each havebeen added. Hence computations have been conducted for a population estimate of 34,718 in 2010
and its lower 5%-ile of 24,530.

53



Provision of the results of a simulation study on the effects of the permit takes on the stockthat takes into account uncertainty

Figure 8 shows projections of the cases considered for the NP sei whales. The calculation was
conducted based on conditioned age-/sex-structured models (see Annexes 14 and 16). Regardless ofparameters assumed, there is no serious difference in the median trajectory between the two catch
scenarios (0 and 134 per year) over the 12 years research period, and therefore it is evident that the
impact of an annual catch of 134 whales is very small in relative terms.

Figure 8. Population trajectory for the sei whales for 50 years under MSYR(1+)=1%. The black line
shows the median trajectory with the proposed catch of 100 replicates (gray lines). The green line is
the median for no catch. The horizontal dashed line shows the carrying capacity for the 1+ population.

Figure 9. Precautionary evaluation of population trajectory for the sei whales by hitting the lower 5%tile of abundance estimate in 2010.

54

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