SC/J09/JR21
Development of an ecosystem model of the western North PacificMori, M.1, Watanabe, H.2, Hakamada, T.1, Tamura, T.1, Konishi, K.1, Murase, H.1 and Matsuoka, K.1

1: The Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo, 104-0055, Japan
2: National Research Institute of Far Seas Fisheries, 2-12-4 Fukuura, Kanazawa, Yokohama 236-8648, Japan
Corresponding author: mori@cetacean.jp

0. Abstract
In order to evaluate the possible impact of whales (minke whales, Bryde’s whales, sei whales and sperm whales)
migrating to the JARPNⅡsurvey area on Japan’s fisheries resources (e.g. anchovy, Pacific saury, mackerels, etc.),
an initial ecosystem model of the western North Pacific is built using the Ecopath-with Ecosim software. As for
an initial test run, the impact of no harvesting and harvesting 4% of the whales for the coming 50 years on catchof the fishes was made. When running the harvesting scenario, uncertainties in the functional response forms
and trophic flow are considered. Also, sensitivity of the results to uncertainties in the in-put parameters is
explored.

Furthermore, MSY which includes and excludes species interactions are calculated. The results

shown here suggest that in average terms: 1) when minke whales are the only species that are harvested by 4% of
its biomass (catch of other species are kept constant at current catch rate), depending on the functional response
form assumed for the species, it is not certain whether catch of some Japanese fisheries resources (e.g. anchovy,
Pacific saury, skipjack tuna, mackerels) will increase or not; 2) when sei and Bryde’s whales are each the only
species that are harvested by 4% of their biomass, regardless of the functional response form assumed for the
species, catch of anchovy, skipjack tuna, and mackerels may increase;3) when minke, sei and Bryde’s whales are
all harvested by 4% of their biomass, positive amount of increase in catch is expected for most of the fish
resources (i.e. anchovy, skipjack tuna and mackerels), indicating the effectiveness of harvesting several whale
species simultaneously; and 4) when sperm whales are the only species that are harvested by 4% of its biomass,depending on the functional response form assumed for the species, catch of anchovy, Pacific saury, mackerels
and skipjack tuna may decrease, but instead, catch of neon-flying squid may increase.

Caveats pertaining to theresults and the use of such ecosystem models in a management context are also discussed.

The main advantages

in building such a model is that it allows quantitative evaluation of the possible effects of whaling on fisheries
resources, and that it provides quantitative estimates on the biomass of small surface squid and mid-deep watersquid which in other ways not have been possible. In addition, for minke and Bryde’s whales, obtaining further
diet composition data has improved the precision of the estimate of % increase in catch of Japan’s fisheries
resources (i.e. Pacific saury, anchovy, mackerels) calculated by the EwE model.

This suggests that continuationof the JARPN2 survey will likely contribute in improving the precision of the effect of whaling on Japan’s
fisheries resources, which is important for robust management of the fisheries resources in the western North
Pacific.
1. Introduction
1-1. Background on JARPN and JARPNIIThe Japanese Whale Research Program under Special Permit in the western North Pacific (JARPN) started in
1994 and ended in 1999. The initial objective of JARPN was to clarify the stock structure differentiation andmixing of minke whales distributed in the waters around Japan (The Government of Japan 1994). Anotherobjective was added in 1996 which was to start a feasibility study on feeding ecology of minke whales.

There

were two main findings from JARPN. One was that scientific information obtained from JARPN enabled theScientific Committee (SC) of the International Whaling Commission (IWC) to discard the sub-stock hypothesison the Okhotsk Sea/Western Pacific (‘O’ stock) of minke whales which was proposed in 1993 (IWC 1997).
Another finding was that most of the whales pursued single prey species aggregation and the main prey species ofminke whales changed seasonally and geographically, for example, anchovy in May/June and Pacific saury in
July/August.

These species are the main target species for the Japanese fisheries, and the estimated prey

consumption by minke whales was comparable to that of the commercial fisheries (Tamura and Fujise 2000a, b).
The IWC/SC reviewed the final results of JARPN during a workshop conducted in 1999, and recommended thatfurther research is necessary to examine the hypothesis of the occurrence of a western stock (‘W’ stock) of minke
whales in offshore areas of the western North Pacific (IWC 2000a). Also, it was agreed that the sampling regime
must be designed to allow for a more quantitative estimation of temporal and geographical variation in diet ofminke whales, and an improved understanding of the distribution and abundance of relevant prey species isnecessary (IWC 2000a). Therefore, it was recommended that acoustic and trawl surveys, designed to address
such questions, should be conducted concurrently with future whale surveys, if possible (IWC 2000a).
To follow up on these issues, the second phase of this research program (JARPNII) started from 2000 and
ended its first period in 2007, with 2000 and 2001 being a feasibility study period. The overall goal of JARPNII
is to contribute to the conservation and sustainable use of marine living resources including whales in the western
North Pacific, especially within Japan’s EEZ. Toward this overall goal, three objectives are set, (a) feeding
ecology and ecosystem studies, (b) monitoring environmental pollutants in cetaceans and the marine ecosystemand (c) elucidation of stock structure (Government of Japan 2002). The priority is put on the first objective
which includes estimation of prey consumption and prey preference of cetaceans, an assessment of how cetaceans
use their habitats through feeding activities, and building an ecosystem model. In this paper we focus on theecosystem modeling aspect of JARPNII.

1-2. Background on ecosystem modeling in JARPNII and its objectives
There are mainly three motivations in building an ecosystem model in the western North Pacific.

The first

motivation is the drastic decrease of Japan’s fisheries catch from 12,785 thousand tons in 1988 to 6,684 thousand
tons in 1998. Due to this decrease, the Fisheries Agency announced the principle for the fundamental policy on
fisheries and its action program to implement the policy, which first priority is given to science-based
management and sustainable utilization of fisheries resources within Japan’s EEZ. To aid the recovery of theresources, investigations should be carried out taking into account the management and sustainable utilization ofwhole ecosystem including marine mammals.
The second motivation is the possibility of competition between marine mammals and fisheries on their prey andfisheries resources. Tamura and Ohsumi (1999) estimated that the amount of yearly consumption of prey bywhales is three to five times larger than the world’s fisheries total catch suggesting that the effect of consumption
by whales on fisheries resources may not be negligible. Also, during JARPN it was found that there was great
overlap between the fishing ground of Pacific saury and the distribution of minke whales, suggesting some2possibility of competition between fisheries and minke whales on Pacific saury.

Furthermore, during thefeasibility study of JARPNII, it was found that not only minke whales but also Bryde’s whales and sei whales
largely prey on Japanese anchovy.

Thus, there is great interest mainly by fisheries managers on investigating theimpact of whaling on Japan’s fisheries resources (e.g. Pacific saury, anchovy etc.).
The third motivation in building an ecosystem model is the world-wide recognition of the importance ofapplying Ecosystem Approach to Fisheries (EAF).

Evaluating the effect of fishing on not only the target

fisheries resource but also on the ecosystem surrounding the species has become an important issue (e.g. bycatchof sea-turtles and sea birds by longline fisheries, destruction of leefs by deepsea trawl etc.). Also, there has beensome criticism on Maximum Sustainable Yield (MSY) calculated from single-species dynamics model (Matsuda
2004), since for example; the MSY of minke whales which mainly prey on Pacific saury may depend on thefishing mortality of Pacific saury, thus can not be appropriately calculated without taking into account interactions
between its prey species.

Moreover, at the World Summit on Sustainable Development in Johannesburg, South

Africa in 2002, it was agreed to encourage the application of EAF by 2010 (WSSD 2002).
With all these motivations and backgrounds, ecosystem modeling in the western north Pacific started. As a firststep in building an ecosystem model, we set mainly two initial objectives.

One is to evaluate the possible impactof whaling on Japan’s fisheries resources, and the second is to explore whether MSY of a species calculated fromsingle-species assessments differ to those calculated from multi-species assessment.

As for a longer-term

objective, we aim to apply such kind of ecosystem model for sustainable management of whales and fishes in thewestern North Pacific.
In this work of nature, Okamura et al. (2001) developed an Ecopath with Ecosim (EwE) model in sub-area 7 (seeFigure 1) to investigate whether there can be the competition between whales and fisheries over marine resourcesin the western North Pacific area. Their results suggested that when the vulnerability parameter is high (which islikely to be the case in the western North Pacific area), competition between cetaceans and fisheries over marine
resources could occur.

However, they also noted that further research on the effect of uncertainty of theparameters and other aspects (e.g. effect of environmental factors) on the results needs to be investigated.
In this paper, we update/expand the EwE model of Okamura et al. (2001) in various aspects as detailed in thefollowing sections.
2. Methodology
We use Ecopath with Ecosim Ver 5.1 (Christensen et al. 2005）in building the model.

Ecopath with Ecosim(EwE) is an ecological software suite, and has three main components: Ecopath – a static, mass-balanced snapshotof the system; Ecosim – a time dynamic simulation module for policy exploration; and Ecospace – a spatial andtemporal dynamic module primarily designed for exploring impact and placement of protected areas (Christensen
et al. 2005). It is currently dominating attempts worldwide to provide information on how ecosystems are likely
to respond to changes in fishery management practices (Plagányi 2007).

Brief description of the model is given

in Appendix A (for details refer to Christensen et al. 2005).

Area to be modeled is the Off shore area of sub-area 7 (which excludes the continental shelf), and sub-areas 8
and 9 (see Figure 1, total area to be modeled is 2,775,043 km2).

Coastal areas and Okhotsk area (sub-area 11)

are not modeled here since the ecosystem and the composition of the species differ from that of the off-shore areas.
3Effect of migration to these areas will be considered at a later stage of development of the model. The model
consists of 31 species (or group) ranging from detritus to whales which are considered to be important species in
the off-shore area.

List of the species (or group) considered in the model are shown in Table 1a. Many of thedata used especially for whales and its prey species are obtained from JARPNII, and the in-put data on
B (biomass), P B (ratio of production to biomass,) Q B (ratio of consumption to biomass), DC ji ( fraction of theprey i in the diet of predator j), and Y (total fishery catch) for each species are shown in Tables 1a-1c (seeAppendix B for detail sources of these data).

By using Ecopath, first a mass-balance model which reflects the current situation of the ecosystem (around year2006) is constructed.

Further, the connectivity of the species and the possible qualitative impact of an increase in

one species may have on the other is evaluated using mixed trophic impact (MTI; see Appendix A for details).
Next, by using Ecosim, the possible impact of whales on their prey species (especially those that are commercially
important for Japanese fisheries) are evaluated. More specifically, the difference in catch of the prey species
between the following two catch scenarios for minke whales, Bryde’s whales, sei whales and sperm whales areevaluated:
1) No catch of the target whale species for 50 years, and
2) Harvest 4% of the biomass of the target whale species every year for 50 years.
4% is chosen here, since the plausible range of Maximum Sustainable Yield Rate (MSYR) for baleen whales iscurrently considered to be 1-7% in the IWC (IWC 1994), and for North Pacific minke whales, it is agreed in 2003
that 4% had a high plausibility (IWC 2004).

When running the harvesting scenarios, three types of uncertainties are considered:
1) Uncertainties in the in-put parameters of Ecopath,2) Uncertainties in the functional response form, and
3) Uncertainties in the trophic flow (e.g. top-down or bottom-up control).
For the various functional response forms, parameter settings in Ecosim and its assumptions are detailed inAppendix C. The trophic flow in Ecosim is controlled by the vulnerability parameter (v). Low v assumes thatthe species is less vulnerable to its predators (‘bottom-up control’) and high v assumes ‘top-down control’.

v=2

indicates mixed trophic control.
As a “Reference case” scenario, we assume Type I functional response with v=2. As a sensitivity test, wechange the functional response form and v.

Whether, MSY of a species calculated from single-species

assessments differ to those calculated from multi-species assessment is also explored.
3. Results
3-1.Building a mass-balance Ecopath model
At the first stage of the mass-balance model development, ecotrophic efficiency (EE) (EE: proportion of ones
production used by consumption or harvesting) for anchovy (less 8cm) slightly exceeded 1 (EE=1.014). If EE>1,
this means that the species is harvested or consumed more than its production. Thus, by increasing the biomassor production of the species, or by reducing the predation mortality of the species, the parameter should be
adjusted to give a mass-balance model.

As for anchovy (less 8cm), P/B was increased from 1.6 to 1.7 since this4

was judged to be the most uncertain parameter among those that were adjustable for this species. The resultant
mass-balance model and its estimated parameters are shown in Table 2.

Figure 2 describes the food-web assumed in the model and the trophic level (TL) of each species calculatedusing Ecopath. Figure 3 indicates the possible impact of direct and indirect interactions (including competition)
in a steady-state system calculated using MTI. The species on the vertical axis shows the species that give
impact, and the species on the horizontal axis shows those that receive the impact.
positive impact, and downward bars represent negative impact.

Upward bars represent

The following can be said from Figure 3:① Increase in the biomass of Pacific saury will give positive impact on minke whales.
② Increase in the biomass of anchovy will give positive impact on minke whales, Bryde’s whales, sei whales,
albacore and skipjack tuna.
However, these relationships shown in Figure 3 only stands when the system is in balance, and should not be used
to predict what will happen in the future if certain interaction terms are changed (Christensen et al. 2005). Thisis explored using Ecosim and the results are shown in the following sections.
3-2.ECOSIM calculation
Possible impact of whaling on the catch of other species in the ecosystem
Percent change in catch between no harvesting and harvesting 4% of the biomass of the whales for the coming
50 years is shown in Figures 4.1 to 4.6 for minke whales, sei whales, Bryde’s whales, sperm whales and the
combined effect of harvesting several whales simultaneously is also explored.

When 4% of the minke whale population is harvested (this amounts to about 440 whales for the initial
harvesting year; which is two times the current amount of catch), the catch of their main prey, Pacific saury may
increase for about 1.4% (i.e. 5,500 tons) for the “Reference case” in 50 years, compared to the case when minke
whales were not harvested (Figure 4.1 upper column figure). For lower v (v=1), this effect is less obvious, andfor higher v (v=5), the increase in the catch of Pacific saury may increase to about 9.5% (i.e. 36 thousand tons).
Catch of several other species, for example sardine, mackerels and anchovy may also slightly increase due toreduced predation mortality by minke whales.

On the other hand, the decrease in the catch of salmon shark iscaused by indirect effect of the decrease in euphausiid biomass due to increased predation mortality by Pacificsaury, which in turn causes the reduction of the biomass of large surface squid, which is the main diet of thesalmon sharks.

When different functional response forms are considered, the increase in the catch of Pacificsaury range from 0-6.5% (i.e. 0-28,000 tons) in 50 years (Figure 4.1 bottom column figure). Increase in thecatch of anchovy is about 0-1%, (i.e. 0-2,800 tons) and that of mackerels ranges from 0-3% (i.e. 0-11,100 tons).
Slight increase (i.e. 2,800 tons) in the catch of skipjack tuna is caused by the increase in the biomass of anchovy,
which is its important prey.

When 4% of the sei whale population is harvested (this amounts to about 280 whales for the initial harvesting
year; which is almost three times the current amount of catch), the catch of their main prey anchovy increase forabout 4% (i.e. 8,300 tons) for the “Reference case” in 50 years, compared to the case when sei whales are notharvested (Figure 4.2 upper column figure).

The increase in the catch of sardine (6%; 2,800 tons), mackerels5(4%; 17,000 tons) and skipjack tuna (8%; 8,300 tons) is also expected. Again for lower v (v=1), this effect is lessobvious, and for higher v (v=5), the change in the increase in the catch of anchovy is slight, whereas mackerels
catch increases for about 19% (i.e. 75,000 tons), as well as that of skipjack tuna and albacore. The increase in
the catch of mackerels is caused by reduced predation mortality by sei whales, whereas the increase in the catch of
skipjack tuna and albacore are caused by the indirect effect of increase in the biomass of anchovy. Catch ofPacific saury slightly decreases due to the increase in minke whale biomass caused by the increase in the biomassof mackerels and anchovy.

To obtain positive catch from Pacific saury as well, minke whales may also need tobe harvested more than the current harvesting rate (the result of the combined effect of harvesting sei and minke
whales together is shown in later scenarios).

When different functional response forms are considered, major

qualitative results do not change but the magnitude of the increase in catch varies (Figure 4.2 bottom column
figure).

Increase in the catch of anchovy range for about 1-10% (i.e. 2,800-19,000 tons), that of mackerels range

for about 4-31% (i.e. 17,000-114,000 tons), that of skipjack tuna range for about 5-16% (i.e. 5,600-17,000 tons) in
50 years, all showing positive catch regardless of the assumption of the functional response form.

When 4% of the Bryde’s whale population is harvested (this amounts to about 120 whales for the initial
harvesting year; which is almost three times the current amount of catch), catch of anchovy (3%; 5,600 tons),
mackerels (2%; 8,300 tons) and skipjack tuna (5%; 5,600 tons) increase for the “Reference case” scenario in 50
years (Figure 4.3 upper column figure). Instead, the catch of sardine decreases for about 6% (i.e. 2,800 tons).
This decrease is caused by the increase in the biomass of minke and sei whales which both slightly consumesardine (and currently only these two whale species is assumed to feed on sardine in the model).

This again

suggests that to obtain positive catch from sardine as well, minke and sei whales may also need to be harvested
more than the current harvesting rate (the result of this combined effect of harvesting Bryde’s, sei and minke
whales is shown in later scenarios).

For higher v (v=5), catch of mackerels and skipjack tuna more than double,

and anchovy catch increases for about 5% (i.e. 11,000 tons). When different functional response forms are
considered, anchovy catch increases for about 1-10% (i.e. 2,800-19,000 tons), that of mackerels for about 2-15%(i.e. 8,300-61,000 tons), and that of skipjack tuna for about 5-18% (i.e. 5,600-19,000 tons) (Figure 4.3 bottom
column figure) again all showing positive catch regardless of the assumption of the functional response form.

When minke, sei and Bryde’s whale populations are all harvested simultaneously for 4% of their biomass, catchof Pacific saury, anchovy, sardine, mackerels and skipjack tuna all increase for about 10% for the “Referencecase” in 50 years compared to when none of these whales are harvested (Figure 4.4 upper column figure).

This

result suggests that rather than harvesting each whale species separately, it is effective when all three whale
species above are harvested in combination.

When different functional response forms are considered, anchovy

catch increases for about 4-19% (i.e. 8,300-39,000 tons), mackerels catch increase for about 7-55% (i.e.
31,000-191,000 tons), Pacific saury catch increase for about 0-2% (i.e. 0-8,300 tons), sardine catch increases forabout 0-12% (i.e. 0-5,600 tons), and skipjack tuna catch increase for about 10-39% (i.e.11,000-39,000 tons)(Figure 4.4 bottom column figure).

When 4% of the sperm whale population is harvested (this amounts to about 2,000 whales for the initial
harvesting year; which is 200 times the current amount of catch), catch of neon-flying squid increase for about 6%6

(2,800 tons) for the “Reference case” scenario in 50 years due to reduced predation mortality by sperm whales(Figure 4.5 upper column figure).

When stronger top-down effect is considered (v=5), biomass of large surface

squid decreases due to increase in predation mortality by neon-flying squid. Catches of blue shark increases forabout 50% (5,600 tons) due to the increase in the biomass of their prey of mid-deep water squid which is reduced
from predation mortality by sperm whales.

Catch of Pacific saury decrease due to the decrease in their main

prey euphausiids biomass caused by the increase in the predation mortality by mid-deep water squid released from
sperm whale predation. When different functional response forms are considered, increase in blue shark catch
range from 0-100% (i.e. 0-14,000 tons), and that of neon-flying squid range from 5-23% (i.e. 5,600-25,000 tons)(Figure 4.5 bottom column figure).

When 4% of all the whales caught during the JARPN II survey (i.e. the above four whale species) were
harvested simultaneously, catch of skipjack tuna, neon-flying squid, mackerels and anchovy increases regardlessof the functional response form assumed; however, for Pacific saury, for most of the case catch decreases (this isdue to the effect of sperm whale harvesting explained above) (Figure 4.6).
Uncertainties in the in-put parameter
The analysis up-to now does not take into account uncertainties in the in-put parameters. Thus, to take thisuncertainty into account, the basic input parameter, B, P/B, Q/B and EE were varied by -50% and +50% (in stepsof 10%) for each component and the resulting percent change in the “missing” estimated parameters to theoriginal parameter were calculated.

The result of this analysis is summarized in Figure 5.

The index is the sumof the maximum change in (estimated parameter – original parameter)/(original parameter) given ±10-50%
changes in the input parameters of the species named on the y-axis.

Figure 5 shows that changes in theparameters of sperm whale and mid-deep water squid exert the greatest influence on the estimated parameters(though note that the constraint of EE<1 is not considered here).
Because the Ecopath mass-balance was most sensitive to parameters for sperm whale and mid-deep water squid,
we concentrated further sensitivity analysis on these two components. The sensitivity of the results of thevarious harvesting scenarios shown in the previous section to changes in the basic parameters for these twocomponents was explored. B, P/B and Q/B for the sperm whale were altered by ±50%, and how the results
shown in the previous section will change was evaluated.

P/B, Q/B and EE for mid-deep water squid were also

altered by ±50%.
Table 3 shows the sensitivity test scenarios and the resultant percent change in catch compared to the result of
the “Reference case” scenario for the harvesting scenarios that actually did have some effect. Other harvesting
scenarios (i.e. harvesting minke, sei and Bryde’s whales) that are not shown in this Table did not have any effectby this change. This result was predictable beforehand, since the diet of sperm whales hardly overlap with thoseof minke, sei or Bryde’s whales.

For the sperm whale harvesting scenario, sensitivity test which reduced 50%

of P/B of mid-deep water squid (s8) resulted in the largest change (-1.3%) in the catch of Pacific saury.

Inabsolute terms this amounts to only a 2% (8,300 tons) decrease in the catch of this species. At maximum, catchof large-surface squid increased 2% compared to the “Reference case” scenario, which amounts to in absoluteterms 4% (5,600 tons) increase in the catch of this species.

Catch of neon-flying squid at maximum,

decreased/increased 2.8% compared to the “Reference case” scenario, which amounts to in absolute terms 3%7

(3,000 tons) or 8% (8,000 tons) increase in the catch of this species. Catch of mackerels at maximum decreased
1.3% compared to the “Reference case” scenario, which amounts to in absolute terms 1.3% (5,600 tons) decreasein the catch of this species.
For the four whale species harvesting scenario, at maximum, catch of mackerels and Pacific saury
decreased/increased 0.7% compared to the “Reference case” scenario, which amounts to in absolute terms 6.6%
(28,000 tons) or 5.3% (22,000 tons), and ±0.7% (±2,900 tons) increase in catch for mackerels and Pacific saury,respectively.

Catch of large-surface squid and neon-flying squid also changed for some scenarios, showing

similar behavior to the sperm whale harvesting scenario.
Single-species MSY and multi-species MSY
Single-species MSY is calculated by the following procedure.

First, for each harvested species, a longsimulation (1000+ years) was run, where fishing mortality rate (F) of that species was incremented or
decremented slowly, while holding all other F (and biomasses) constant at Ecopath base values. FMSY for the
species was taken to be the F that resulted in maximum average catch.

Multi-species MSY is calculated by

setting the F for each harvested species to its FMSY estimated from the above simulation for that species.
Biomasses and catches at the end of this simulation represent Ecosim predicted equilibrium values under the
all-species FMSY policy.
Figure 6 shows the ratio of single-species MSY to multi-species MSY for the harvested species. For most of
the whale species and for some fish predators, single species MSY is estimated to be higher than the multi-species
MSY. This result indicate that for most of the whales, MSY calculated from the single-species analysis could be
optimistic, since it ignores the effect of predation mortality by other predators and fishing mortality on its preyspecies.

MSY calculated from the single-species analysis for anchovy could also be optimistic, since it ignoreshigh predation mortality by various predator species, including whales, albacore and skipjack tuna.
4. Summary and Discussions
One of the advantages in building such ecosystem model is that it allows quantitative evaluation of the possible
effect of whaling on fisheries resources. Without such ecosystem model, predictions are often only qualitative.
Table 4 summarizes the possible change in catch for some fish and squid resources between harvesting 4% of thebiomass of the whales and no harvesting of the whales for the coming 50 years. The results shown in Table 4
assumes v=2 (mixed trophic control) since our aim was not to show results on extreme cases, such as fully
bottom-up control or strong top-down control, but to show results on intermediate cases, since it is thought thatthe relative strength of bottom-up or top-down control in the western North Pacific area may vary by season, areaand by year (Nagasawa pers. commn.).

Thus the results shown here may be regarded as an average case.

Several interesting aspects can be seen from Table 4:l

When minke whales are the only species that are harvested by 4% of its biomass (catch of other species are
kept constant at current catch rate), depending on the functional response form assumed for the species, it is
not certain whether catch of some important Japanese fisheries resources (e.g. anchovy, Pacific saury,skipjack tuna, mackerels) will increase or not.

l

When sei and Bryde’s whales are each the only species that are harvested by 4% of its biomass, regardlessof the functional response form assumed for the species, catch of anchovy, skipjack tuna, and mackerels will8

increase.
l

When minke, sei and Bryde’s whales are all harvested by 4% of its biomass, positive amount of increase incatch is expected for most of the fish resources (i.e. anchovy, skipjack tuna and mackerels), indicating theeffectiveness of harvesting several whale species simultaneously.

lWhen sperm whales are the only species that are harvested by 4% of its biomass, depending on thefunctional response form assumed for the species, catch of anchovy, Pacific saury, mackerels and skipjack
tuna may decrease. Instead, catch of neon-flying squid may increase.

These results are all deterministic and do not consider stochastic effects on species which could be caused bysome changes in environmental factors, such as water temperature or water salinity etc.

Thus, the results shown

here should not be regarded as definitive, but only to indicate average and directional possible response on
whaling to fisheries resources. At the current stage, prediction on the possible change of environmental factors
is limited as well as how the species will respond to such environmental effect is not known. Pelagic fish
resources in the research area have shown drastic fluctuations in a process called “species replacement” for the
past 100 years (Wada 1997), but the mechanism that causes this replacement is still under investigation.

In thissituation, the result obtained from conducting stochastic simulations largely depends on the magnitude of thegiven stochasticity by the user, and often the prediction is less meaningful.

As more knowledge on therelationship between environmental factors and its effect on species become available, it should be included insuch ecosystem model.

Also, it is very important to continue monitoring the diet composition of the species inthis area including top predators, such as whales, since once “species replacement” occurs, it is known that dietcomposition may drastically change, and the model predictions are very sensitive to these diet compositions.

In

this sense, continuation of survey such as JARPN II is essential for building a meaningful ecosystem model,
which will in turn be used for sustainable management of whales and fisheries resources in the area.
In addition, accumulation of long-term relative abundance estimates for the species in this area is important inunderstanding and monitoring the ecosystem structure.

At the current stage, data on long-term relative

abundance estimates only exists for sardine and mackerels for the species considered in this model. This lack oftime-series data on relative abundance precludes the model to be fit to past trend estimates in an any meaningful
way. Thus, attempt to estimate one plausible v or functional response form by fitting to past trend data is notconducted in this analysis, but rather our aim is to give plausible ‘ranges’ of results which could all be equallypossible under the current situation of various uncertainties. There are many studies in the past that have used
EwE to predict fisheries impact on the ecosystem, but none of the studies have actually explored the effect ofassuming various functional response forms to their results. As to the knowledge of the authors, this study is thefirst in exploring the effect of assuming various functional response forms on the results.

Furthermore,

information on whether prey-preference exists or not for the species considered in the model (especially whales),and if it exists, in what circumstances does this occur (e.g. how rare must one prey species become compared tosome alternative prey species) will help identify plausible functional response form to assume in the model andwould help to validate the results obtained from the model. In such sense, continuation of the concurrent whaleand prey survey in JARPNII is necessary.
Since this was considered to be a first step in building such ecosystem model, we tried to start the model as
simple as possible.

Thus, effect of whales migrating to areas outside the modeled area is not considered in this9

analysis. Also, lack of quantitative data for areas outside the modeled area and its possible difference in species
compositions, precluded in expanding the modeled area. However, we believe that the modeled area covers themain feeding area of the whales considered in the model.
Another advantage in building such ecosystem model is that it provides quantitative estimates on the biomass of
small surface squid and mid-deep water squid which in other ways not have been possible, and will contribute infurther understanding of the meso-pelagic ecosystem.
Comparison with results obtained from the MRM
An ecosystem model can be developed in various scales.

Some models may include detail dynamics of thenutrients and lower-trophic level species, some models may ignore its details and assume that its biomass isconstant or fluctuate in a certain stochastic manner.

Moreover, some models may include detailage/length-structure of the species, and some models may ignore those details.

Plagányi (2007) gives a goodsummary on various ecosystem models which have been developed worldwide, and discusses their plausibility inuse of the model in an EAF context.

Moreover, FAO (2008) suggests that there is no one single right model, andthat the greatest leverage is gained by considering combinations of models that may be of quite different forms.
Kawahara et al. (2008) develops an MRM for the same area considered in this analysis. Hardly any previous
studies have built two different structures of ecosystem models (e.g. EwE and MRM type) for a single area, andthis is one of the unique and appealing characteristic of JARPNII. Comparison with results obtained from
Kawahara et al. (2008) is one of important future tasks.
Application of such model in a management context
In general, ecosystem models are potentially important tools for providing wider scientific information on
fisheries management such as impacts of the fishery on other ecosystem components and to take into accountchanges in the ecosystem other than those caused by fishing that may be impacting the fishery. Also, it can beused to simulate the implications and trade offs of alternative management actions and trade-offs for the different,
conflicting stakeholders or objectives, and in this way, they can provide valuable information to managers in the
search for optimal management measures and approaches (FAO 2008).

Moreover, ecosystem models can havean important role to play in Management Strategy Evaluation (MSE) or the Management Procedure (MP)approach, especially as Operating Models (OMs) which provide the basis for simulation testing to assess how wellalternative candidate harvest rules achieve the objectives sought by the management authority (FAO 2008).
The ecosystem model built in this study is not suitable for calculating detail total allowable catch (TAC) for
species considered in the model.

Rather, it may be used as for example as an OM to test whether TAC calculated

from Revised Management Procedure (RMP) of some whale species do not deteriorate or give negative impact onother species in the ecosystem.

If this was to give any negative impact on some other species in the ecosystem,

TAC calculated from RMP may be revised. By doing so, this will ensure sustainability of the ecosystem
including whales and fisheries resources. Also, such ecosystem model may be used for exploring possible
trade-offs between various harvesting scenarios. As Table 4 shows, depending on the harvesting scenario,
possible effect of whaling on fisheries resources varies, and the catch of some species may increase or decrease.
Thus, such model may be used as a tool by fisheries managers in deciding what kind of fishing strategies to take,considering various trade-offs in catch between the species.
105. Future tasks
As briefly noted in the previous section, whales considered in this analysis also migrate to areas other than themodeled area. As more data accumulate for other areas, this effect will be considered. Also, some of the in-put
data especially for the low-trophic level species are still primitive and more work needs to be done in collecting
more appropriate data. Moreover, as more knowledge on the relationship between environmental factors and its
effect on species become available, it will be included in such ecosystem model. Comparison with results
obtained from Kawahara et al. (2008) is also expected.
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Table 1a. Input data on B, P/B, Q/B used in Ecopath. Shaded part is estimated from the model. Italic part iscalculated within Ecopath using the assumptions that (1) body growth for the species as a whole follows a von15

Bertalanffy growth curve with weight proportional to length-cubed; and (2) the species population as whole has
had relatively stable mortality and relative recruitment rate for at least a few years, and so has reached a stableage-size distribution. The sources of the data are explained in Appendix B. EE of mid-deep water squid isassumed to be low (0.2) since there is no fishery for this resources and also it is considered that the proportion of
the production that is used within the system for this species is low.

English name
1 Minke whale
2 Bryde's whale
3 Sei whale
4 Other baleen whales
5 Sperm whale
6 Baird's beaked whale
7 Short-finned pilot whale
8 Ziphiidae
9 Other toothed whales
10 Northern fur seal
11 Marine birds
12 Albacore
13 Sword fish
14 Skipjack tuna
15 Blue shark
16 Salmon shark
17 Lanternfish
18 Neon flying squid
19 Large surface squid
20 Small surface squid
21 Mid-deep water sea squid
22 Mackerels
23 Pacific pomfret
24 Sardine
25 Anchovy (<8cm)
26 Anchovy (>=8cm)
27 Pacific saury
28 Phytoplankton
29 Euphausiids
30 Copepods eaten by whales
31 Other Copepods
32 Detritus

Biomass inhab.area
0.011
0.047
0.056
0.068
0.263
0.025
0.009
0.031
0.040
0.002
0.004
0.059
0.007
0.079
0.016
0.005
5.405
0.097
0.360

Habitat area (fraction)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

0.366
0.136
0.048

0.369
1.405
1.586
26.58040.67021.29721.297132.900

16

Production Consumption Ecotrophic/biomass/biomass
efficiency
0.120
5.20
0.100
4.38
0.060
4.16
0.057
3.80
0.102
4.20
0.110
5.53
0.100
7.42
0.100
7.06
0.100
11.03
0.235
39.03
0.100
54.57
0.410
1.94
0.600
2.05
0.900
6.00
0.390
3.10
0.390
5.10
2.680
13.35
3.620
10.80
2.480
10.80
3.650
10.80
0.9
2.560
10.80
0.2
1.030
6.87
0.750
3.75
0.770
5.14
1.600
28.02
1.600
14.04
0.740
10.00
85.960
2.555
12.05
5.000
10.00
5.000
10.00
-

Table 1b. Diet composition matrix used in the Ecopath model. The sources of the data are explained in Appendix B.
Prey / predator
1
1 Minke whale
2 Bryde's whale
3 Sei whale
4 Other baleen whales
5 Sperm whale
6 Baird's beaked whale
7 Short-finned pilot whale
8 Ziphiidae
9 Other toothed whales
10 Northern fur seal
11 Marine birds
12 Albacore
13 Sword fish
14 Skipjack tuna
15 Blue shark
16 Salmon shark
17 Lanternfish
18 Neon flying squid
19 Large surface squid
0.02
20 Small surface squid
21 Mid-deep water sea squid
22 Mackerels
0.04
23 Pacific pomfret
0.00
24 Sardine
0.00
25 Anchovy (<8cm)
0.00
26 Anchovy (>=8cm)
0.29
27 Pacific saury
0.46
28 Phytoplankton
29 Euphausiids
0.10
30 Copepods eaten by wh 0.02
31 Other Copepods
32 Detritus
Import
0.06
Sum
1.00

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

29

30

31

0.00
0.00

0.49 0.12 0.05 0.01 0.01 0.09
0.04 0.00

0.11 0.30

0.12 0.02 0.38

0.00 0.01 0.06 0.01 0.82 0.07 0.19
0.01

0.06 0.21 0.04 0.10 0.79

0.19

0.86 0.02 0.01 0.81

0.16 0.01 0.02 0.01 0.04 0.03
0.16

0.05 0.07

0.06

0.00 0.07

0.04 0.12

0.48 0.14

0.28 0.06

0.02 0.03

0.01

0.01 0.04

0.16

0.01 0.23

0.25 0.08

0.01 0.37

0.04

0.01 0.02

0.00 0.00

0.12

0.11

0.05

0.00
0.38 0.31
0.30 0.26 0.05

0.15

0.02 0.05

0.06 0.37

0.42

0.01 0.03

0.03 0.01

0.06 0.44

0.40 0.01

0.14

0.07

0.12

0.02 0.03
0.70

0.25 0.22 0.65

0.03

0.12 0.00

0.09

0.13 0.25

0.24 0.00 0.46 0.06 0.60 0.50 0.00
0.36

0.20 0.15

0.18

0.78 0.20 0.15

0.12 0.26 0.26
0.20 0.20 0.50

0.15 0.10 0.10 0.24 0.09
0.15 0.70 0.70

0.09
0.70 0.74 0.74

0.02

0.04 0.96 0.17 0.01 0.15 0.31

0.05 0.19 0.03 0.18 0.04 0.22 0.09 0.37 0.17

0.02 0.26

0.26

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

17

Table 1c. Whaling and fishery in-put data (t/km2/year) used in the Ecopath model. The sources of the data are
explained in Appendix B.

English name
1 Minke whale
2 Bryde's whale
3 Sei whale
4 Other baleen whales
5 Sperm whale
6 Baird's beaked whale
7 Short-finned pilot whale
8 Ziphiidae
9 Other toothed whales
10 Northern fur seal
11 Marine birds
12 Albacore
13 Sword fish
14 Skipjack tuna
15 Blue shark
16 Salmon shark
17 Lanternfish
18 Neon flying squid
19 Large surface squid
20 Small surface squid
21 Mid-deep water sea squid
22 Mackerels
23 Pacific pomfret
24 Sardine
25 Anchovy (<8cm)
26 Anchovy (>=8cm)
27 Pacific saury
28 Phytoplankton
29 Euphausiids
30 Copepods eaten by whales
31 Other Copepods
32 Detritus

JARPN20.00040.00030.0007

Other whaling

Fishing

6.85E-05
0.0003
6.25E-05
0.000254
0.01
0.00
0.04
0.00
0.00
0.04
0.05
0.15
0.00
0.02
0.01
0.08
0.15

18

Table 2. Parameters of the mass-balance Ecopath model (the values indicated in grey are estimated by themodel).

Group name

Trophic
level

Biomass(t/km2)

Minke whale
Brydes whale
Sei whale
Other baleen whales
Sperm whale
Bairds beaked whale
Short-finned pilot whale
Ziphiidae
Other toothed whales
Northern fur seals
Marine birds
Albacore
Sword fish
Skipjack tuna
Blue shark
Salmon shark
Lanternfish
Neon flying squid
Large surface squid
Small surface squid
Mid-deep water squid
Mackerels
Pacific pomfret
SardineAnchovy (less 8cm)
Anchovy (>=8cm)
Pacific saury
Phytoplankton
Euphausiids
Copepods eaten by whales
Other copepods
Detritus

3.99
3.83
3.73
3.23
4.17
4.15
4.40
4.24
4.46
4.08
4.24
4.08
4.81
3.97
4.27
4.35
3.06
4.12
3.41
3.01
3.11
3.30
4.20
2.30
3.04
3.04
3.12
1.00
2.18
2.00
2.00
1.00

0.011
0.047
0.056
0.068
0.263
0.025
0.009
0.054
0.040
0.002
0.004
0.059
0.007
0.079
0.016
0.005
5.405
0.097
0.360
0.279
2.707
0.366
0.136
0.048
0.381
1.405
1.586
26.58040.67021.29721.297132.900

Prod./ biom. Cons./ biom. Ecotrophic(/year)(/year)efficiency
0.120
0.100
0.060
0.057
0.102
0.110
0.100
0.100
0.100
0.235
0.100
0.410
0.600
0.900
0.390
0.390
2.680
3.620
2.480
3.650
2.560
1.030
0.750
0.770
1.700
1.600
0.740
85.960
2.555
5.000
5.000
-

19

5.200
4.380
4.160
3.800
4.200
5.530
7.420
7.060
11.03039.03054.570
1.940
2.050
6.000
3.100
5.100
13.35010.80010.80010.80010.800
6.870
3.750
5.140
28.19814.04010.00012.05010.00010.000
-

0.303
0.064
0.208
0.000
0.003
0.109
0.069
0.000
0.064
0.000
0.000
0.254
0.130
0.563
0.661
0.717
0.047
0.976
0.738
0.900
0.200
0.557
0.274
0.480
0.925
0.385
0.238
0.074
0.505
0.782
0.817
0.269

Production /consumption
0.023
0.023
0.014
0.015
0.024
0.020
0.013
0.014
0.009
0.006
0.002
0.211
0.293
0.150
0.126
0.076
0.201
0.335
0.230
0.338
0.237
0.150
0.200
0.150
0.060
0.114
0.074
0.212
0.500
0.500
-

Table 3. (a) Sensitivity test scenarios (s1~s12) and (b) the resultant percent change in catch compared to the“Reference case” scenario for harvesting scenarios that did have some effect. Other harvesting scenarios that are
not shown here did not have any effect.
(a)

B

P/B

Q/B

EE

50%

s1

s3

s5

-

-50%

s2

s4

s6

-

50%

-

s7

s9

s11

-50%

-

s8

s10

s12

Sperm whale
Mid-deep water squid(b)(b-1. Sperm whale harvesting scenario)

s1
s2
s3
s4
s5
s6
s7
s8
s9
s10
s11
s12

anchovy(>=8cm)
0
0
0
0
0
0
0
0
0
0
0
0

pacific
saury
-0.7
0
0
-0.7
-0.7
0
0
-1.3
-0.7
0
0
0

skipjack
tuna
0
0
0
0
0
0
0
0
0
0
0
0

mackerels
-0.6
0
0
-0.6
0
0
0
-1.3
-0.6
0
0
-0.6

large surface
squid
2
0
0
2
0
0
0
0
0
0
0
2

neon flying squid
0
-2.8
-2.8
2.8
0
-2.8
0
0
0
0
0
0

(b-2. Minke, sei, Bryde’s and sperm whale harvesting scenario)

s1
s2
s3
s4
s5
s6
s7
s8
s9
s10
s11
s12

anchovy(>=8cm)
0
0
0
0
0
0
0
0
0
0
0
0

pacific
saury
0
0.7
0.7
-0.7
0
0.7
0.7
0.7
0
0.7
0
0

skipjack
tuna
0
0
0
0
0
0
0
0
0
0
0
0

mackerels
0
0.7
0.7
0
0
0.7
0.7
-0.7
0
0.7
0
0

20

large surface
squid
0
-2
0
2
0
-2
0
0
0
0
0
0

neon flying squid
0
-2.8
-2.8
2.8
0
-2.8
0
0
0
0
-2.8
0

Table 4. Summary of change in catch (in % and tons) for some fish and squid resources between harvesting 4% of the biomass of the whales and no harvesting of the whales
for the coming 50 years. Numbers shown in brackets for the whale harvesting scenarios shows the current biomass of the whale and the biomass in 50 years if the species
were harvested annually by 4% of its biomass.
Harvesting scenario/Species

Anchovy (>8cm)
0-1%
Minke whales (29,741t →27,750t) (0-2,800 tons)
1-10%(2,800-19,000
tons)
Sei whales (15,5978t →77,701t)
1-10%Bryde's whales (130,816t→72,151t)(2,800-19,000 tons)
4-19%(8,300-39,000 tons)
Minke, sei, Bryde's whales

Pacific saury
0-6.5%(0-28,000 tons)

Skipjack tuna
0-2.5%(0-28,000 tons)

Mackerels
0-3%(0-11,100 tons)

Neon-flying squidBlue shark---3-(-1)%
5-16%(-12,000-(-4,000) tons) (5,600-17,000 tons)

4-31%(17,000-114,000tons)(-2.5)-0%(-3,000-0 tons)--2-(-1)%(-8,000-(-4,000) tons)

5-18%(5,600-19,000 tons)

2-15%(8,300-61,000 tons)(-2.5)-0%(-3,000-0 tons)--0.6-2%(-2,800-8,300 tons)

10-39%(11,000-39,000tons)

7-55%(31,000-191,000 tons)(-5)-(-3)%(-6,000-(-3,000) tons)

-

-3-5%
(-6,000-11,000
tons)
Sperm whale (729,030t →394,056t)-15-(-1)%(-70,000-(-6,000 tons)-5-5%(-5,600-5,6000tons)-11-(-1)%
5-23%(-44,000-(-2,800) tons) (5,600-25,000 tons)

0-100%(0-14,000 tons)

4-21%
Minke, sei, Bryde's, sperm whales (8,300-42,000 tons)-15-(-1)%(-70,000-(-6,000 tons)

10-42%(11,000-42,000tons)

7-42%(31,000-150,000 tons)

0-100%(0-14,000 tons)

3-15%(3,000-17,000 tons)

21

Figure 1. The research area and strata for the JARPNII surveys (extracted from Government of Japan 2002).
22

Figure 2.Food web assumed in the model and the trophic level of each species estimated by Ecopath.
23

Figure 3. Effect of the short-term increase of the species shown on the vertical axis on the species shown on thehorizontal axis. Upward bars represent positive impact, and downward bars represent negative impact.
24

% change in catch between no catch and taking 4% of min ke wh ale s for 50 years (Type1F.R.)% change inC

v=1

v=2 (Ref)

v=5

20
10
0
-10
-20
-30
-40
-50
-60

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Ziphiidae

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel% change in catch between no catch and taking 4% of min ke wh ale s for 50 years ( v =2)% change in C

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacific
saury

Type1(Ref)

Type2

Type3

Type3+otherM

Type3+switch

default

7
6
5
4
3
2
1
0
-1
-2
-3

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Ziphiidae

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacific
saury

Figure 4.1 Percent (%) change in catch for the harvested species, between no catch and taking 4% of minke whales for the coming 50 years (fishing effort for the otherharvested species is kept as current). The top figure shows sensitivities to changes in the vulnerability (v) parameter, and the bottom figure shows sensitivities to changes in
the functional response function assumed.
25

% change in C

% change in catch between no catch and taking 4% of se i wh ale s for 50 years (Type1F.R.)

v=1

v=2(Ref)

v=5

20
15
10
5
0
-5

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel% change in catch between no catch and taking 4% of se i wh ale s for 50 years ( v =2)% change in C

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)

Type1(Ref)

Type2

Type3

Type3+otherM

Type3+switch

default

Pacific
saury

40
30
20
10
0
-10
-20
-30
-40
-50
-60

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacific
saury

Figure 4.2 Percent (%) change in catch for the harvested species, between no catch and taking 4% of sei whales for the coming 50 years (fishing effort for the otherharvested species is kept as current). The top figure shows sensitivities to changes in the vulnerability (v) parameter, and the bottom figure shows sensitivities to changes in
the functional response function assumed.
26

% change in C

% change in catch between no catch and taking 4% of Bryde 's wh ale s for 50 years (Type1F.R.)

v=1

v=2(Ref)

v=5

20
15
10
5
0
-5
-10

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel% change in catch between no catch and taking 4% of Bryde 's wh ale s for 50 years ( v =2)% change in C

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)

Type1(Ref)

Type2

Type3

Type3+otherM

Type3+switch

default

Pacific
saury

30
20
10
0
-10
-20
-30
-40
-50
-60

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacific
saury

Figure 4.3 Percent (%) change in catch for the harvested species, between no catch and taking 4% of Bryde’s whales for the coming 50 years (fishing effort for the otherharvested species is kept as current). The top figure shows sensitivities to changes in the vulnerability (v) parameter, and the bottom figure shows sensitivities to changes in
the functional response function assumed.
27

% change in catch between no catch and taking 4% of the min ke , se i, an d Bryde 's wh ale s for 50 years (Type1F.R.)% change in C
60
40

v=1

v=2(Ref)

v=5

20
0
-20
-40
-60
-80
-100
-120

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacificsaury% change in catch between no catch and taking 4% of the min ke , se i, an d Bryde 's wh ale sfor 50 years ( v =2)% change in C
60

Type1(Ref)

Type2

Type3

Type3+otherM

Type3+switch

default

40
20
0
-20
-40
-60

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacific
saury

Figure 4.4 Percent (%) change in catch for the harvested species, between no catch and taking 4% of minke, sei and Bryde’s whales together for the coming 50 years(fishing effort for the other harvested species is kept as current). The top figure shows sensitivities to changes in the vulnerability (v) parameter, and the bottom figure
shows sensitivities to changes in the functional response function assumed.
28

% change in catch between no catch and taking 4% of spe rm wh ale s for 50 years (Type1F.R.)% change in C

v=1

v=2(Ref)

v=5

60
50
40
30
20
10
0
-10
-20

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel% change in catch between no catch and taking 4% of spe rm wh ale s for 50 years ( v =2)% change in C

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)

Type1(Ref)

Type2

Type3

Type3+otherM

Type3+switch

default

Pacific
pomfretAnchovy(less 8cm)

Pacific
saury

120
100
80
60
40
20
0
-20
-40

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

MackerelSardineAnchovy(>=8cm)Pacific
saury

Figure 4.5 Percent (%) change in catch for the harvested species, between no catch and taking 4% of sperm whales for the coming 50 years (fishing effort for the otherharvested species is kept as current). The top figure shows sensitivities to changes in the vulnerability (v) parameter, and the bottom figure shows sensitivities to changes in
the functional response function assumed.
29

% change in C

% change in catch between no catch and taking 4% of the wh ale s c au gh t in JARPN2 for 50 years (Type1F.R.)

v=1

v=2(Ref)

v=5

80
60
40
20
0
-20
-40
-60

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel% change in catch between no catch and taking 4% of the wh ale s c au gh t in JARPN2 for 50 years ( v =2)% change in C

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)

Type1(Ref)

Type2

Type3

Type3+otherM

Type3+switch

default

Pacific
saury

120
100
80
60
40
20
0
-20
-40

Minke
whale

Brydeswhale

Sei whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned pilot
whale

Other
toothedwhalesAlbacore

Sword fish

Skipjacktuna

Blue shark

Salmonshark

Neon flying
squid

Large
surfacesquid

Mackerel

Pacific
pomfretSardineAnchovy(less 8cm)

Anchovy(>=8cm)Pacific
saury

Figure 4.6 Percent (%) change in catch for the harvested species, between no catch and taking 4% of the whales caught in JARPN2 (minke, sei, Bryde’s and sperm
whales) for the coming 50 years (fishing effort for the other harvested species is kept as current). The top figure shows sensitivities to changes in the vulnerability (v)parameter, and the bottom figure shows sensitivities to changes in the functional response function assumed.
30

Minke whale (2)Bryde's whale (2)Sei whale (2)Sperm whale (4)Bairds beaked whale (2)Short-finned pilot whales (2)Other toothed whales (2)Marine birds (2)Albacore (2)Sword fish (2)Skipjack tuna (2)Blue shark (2)Salmon shark (2)Lanternfish (2)Neon flying squid (2)Large surface squid (2)Small surface squid (2)Mid-deep water squid (4)Mackerel (2)Pacific pomfret (2)Sardine (2)Anchovy (less 8cm) (2)Anchovy (>=8cm) (2)Pacific saury (2)Phytoplankton (2)Euphausiids (2)Copepods eaten by whales (2)Other copepods (2)
0

0.5

1

1.5
Index of sensitivity

2

2.5

3

Figure 5. Sensitivity analysis results for JARPN2_v3, using an index of sensitivity (the sum of the ratio of the
maximum change in the estimated parameter to the original parameter, given ±10-50% changes in the input
parameters of the components named on the y-axis). The numbers shown in parenthesis on the y-axis is the countof estimated parameters affected by at least 30% by changes in the in-put parameter of B, P/B, Q/B and EE of thespecies named on the y-axis.

31

Single species MSY/Multispecies MSY

2

1.5

1

0.5

0

Minke
whale

Brydes
whale

Sei
whale

Sperm
whale

Bairdsbeakedwhale

Shortfinned
pilot
whale

Other Albacore Sword
fish
toothedwhales

Skipjack
tuna

Blue
shark

Salmonshark

Neon
flyingsquid

Large Mackerel Pacific Sardine Anchovy Anchovy Pacificpomfret(less (>=8cm) saury
surface
8cm)
squid

Figure 6. Ratio of single-species MSY to multi-species MSY for the harvested species.

32Appendix A: Description of the Ecopath with Ecosim (EwE) model
The Ecopath model
Ecopath (Polovina 1984a, 1984b; Christense et al. 2005) parameterizes models based on two master equations,
one ensuring balance among each functional group and one balancing the flows within each functional group.
The first equation ensures balance among groups in the model:(A-1a)Pi = Yi + Bi ⋅ M 2i + Ei + BAi + Pi ⋅ (1 − EEi )where, for group i, Pi is the total production rate, Yi is the total fishery catch rate, Bi is the biomass, M 2 i is thetotal predation rate for group i, E i is the net migration rate (emigration - immigration), BAi is the biomass
accumulation rate and M 0 i = Pi ⋅ (1 − EE i ) is the “other mortality” rate, where EEi being “ecotrophic efficiency”.
Equation (1a) can also be expressed as:
n

P
Q
P
Bi ⋅   −B j ⋅   ⋅ DC ji −   ⋅ Bi ⋅ (1 − EEi ) − Yi − Ei − BAi = 0 B i j =1
 B j B i∑(A-1b)

where P B is the ratio of production to biomass, Q B is the ratio of consumption to biomass, and DC ji is thefraction of the prey i in the diet of predator j.
The second equation, which balances the flows within each group, isConsumption = production + respiration +unassimilated food(A-2)

Qi = Pi + Ri + GSi ⋅ Qi
where, for group i, Qi is consumption, Ri is respiration, and GSi is the proportion of food that is not assimilated.

Each group in the model is represented by the above two balanced equations and requires six input parameters.
Diet composition and catch are mandatory and three of the other four parameters ( B , P B , Q B , and EE ) must beentered.

Representation of multi-stanza life histories
Ecopath with Ecosim users can create a set of biomass groups representing life history stages or stanzas for
species that have complex trophic ontogeny. Users of this feature must enter baseline estimates of total mortality
rate Z and diet composition for each stanza, then biomass and Q B for one “leading” stanza only. For Ecopath
mass balance calculations, Z entered for each stanza-group is used to replace the Ecopath P B for that group.
Further, the B and Q B for all stanza-groups besides the leading (entry) stanza are calculated before entry to
Ecopath, using the assumptions that (1) body growth for the species as whole follows a von Bertalanffy growth
curve with weight proportional to length-cubed; and (2) the species population as a whole has had relatively stablemortality and relative recruitment rate for at last a few years, and so has reached a stable age-size distribution.
Under the stable age distribution assumption, the relative number of age “a” animals is given by lala where∑

where thethe sum is over all ages, and la is the population growth rate-corrected survivorship la = e ∑asum of Z’s is over all ages up to “a” and the BA/B term represents effect on the numbers at age of the population−

Z a − a × BA B∑
growth rate. Further, the relative biomass of animals in stanza s should be b =∑a s ,max

s

l ×w

a
a = a s ,min a,a max
la × wa
a =1

where

wa = [1 − exp(− K × a )]3 is the von Bertalanffy prediction of relative body weight at age a.
Knowing the B for one leading stanza, and the bs for each stanza s, the biomasses for the other stanzas can becalculated by first calculating population biomass B = Bleading s bleading s , then setting Bs = B ⋅ bs for the otherstanzas. Q B estimates for non-leading stanzas are calculated with a similar approach, assuming that feeding
rates vary with age as the 2 3 power of body weight (a “hidden” assumption in the von Bertalanffy growthmodel) (Christensen et al. 2005).
Mixed trophic impact
Once the linear equations are solved and missing parameters are estimated (often EE becomes the estimatedparameter), one can conduct analyses such as ‘mixed trophic impact (MTI)’ using the tool prepared in Ecopath
software. MTI allows assessing the effect that changes in the biomass of a group will have on the biomass of the33other group in the system (for details see Christensen et al. 2005). The MTI for living groups is calculated byconstructing an n×n matrix, where the i, j th element representing the interaction between the impacting group iand the impacted group j , which is expressed as:
MTI i , j = DCi , j − FC j , i ,(A-3)
where DCij is the diet composition term expressing how much j contributes to the diet of i, and FCij is a hostcomposition term giving the proportion of the predation on j that is due to i as a predator.
host compositions the fishing fleets are included as ‘predators’ (Christensen et al. 2005).

When calculating the

The Ecosim model
Ecosim consist of biomass dynamics expresse through a series of coupled differential equations. The equations
are derived from the Ecopath master equation (A-1a) and take the formdBi
= gi ⋅dt∑ Q − ∑ Q + I − (M + F + e )⋅ B
ji

j

ij

i

i

i

i

i

(A-4)

j

where dBi dt represents the growth rate during the time interval dt of group i in terms of its biomass Bi , g i is thenet growth efficiency (production/consumption ratio), M i is the non-predation natural mortality rate, Fi is thefishing mortality rate, ei is emigration rate, I i is immigration rate. The two summations estimates consumption
rates, the first expressing the total consumption by group i, and the second the predation by all predators on thesame group i. The consumption rates, Q ji , are calculated based on the ‘foraging arena’ concept, where Bi ’s are
divided into vulnerable and invulnerable components (Walters et al. 1997, Figure 1), and it is the transfer rate ( vij )between these two components that determines if control is top-down (i.e. Lotka-Volterra), bottom-up (i.e.
donor-driven) or of an intermediate type. The consumption rate is expressed as:
Qij =aij ⋅ vij ⋅ Bi ⋅ Pj ⋅ Ti ⋅ T j ⋅ Sij ⋅ M ij D j
vij + vij ⋅ Ti ⋅ M ij + aij ⋅ M ij ⋅ Pj ⋅ Sij ⋅ T j D j(A-5)

where aij is the effective search rate for predator i feeding on a prey j, vij is the vulnerability expressing the ratewith which prey move between being vulnerable and not vulnerable, Bi is prey biomass, Pj is predator
abundance, Ti represents prey relative feeding time, T j is predator relative feeding time, Sij is user-definedseasonal or long term forcing effects, M ij is the mediation forcing effects, and D j is the effects of handling time
as a limit to consumption rate. For further details refer to (Christensen et al. 2005).

34

Appendix B: Sources of the in-put data used in the model
Minke whale
1) Diet composition
Data from Tamura et al. (2009a) which is based on data collected from JARPNIIsurvey. 46.08% Pacific saury,29.16% anchovy (large), 9.85% euphausiids, 5.48% others, 4.17% mackerelss, 2.43% copepods, 2.35% large
surface squid, 0.27% pacific pomfret, 0.19% anchovy (small), 0.003% sardine.
2) Biomass
The abundance estimate is from Hakamada et al. (2009). This is based on the JARPN2 sighting survey from2002-2007, and the abundance estimate for Areas 7, 8 and 9 (excludes Russian EEZ) is 6,609 with CV=0.620.
Mean body-weight is calculated as 4.5 tons by Tamura et al. (2009a) which is based on data collected fromJARPNII survey. Thus, the biomass is 29,741 tons.
3) P/B or Z
As for natural mortality (M), 0.10 which is the average of the values used in the comprehensive assessment of
NP minke whales is used (IWC 2000b). The current fishing mortality (F) is about 0.02. Thus, Z=M+F=0.12
will be used.
4) Q/B
Data from Tamura et al. (2009a) which is based on data collected from JARPNⅡsurvey will be used. Q/B=5.20(range 4.6-6.3).
5) Catch
220 minke whales are caught every year, with mean body-weight of 4.5 tons. Thus, the total catch is 990
tons/year.
Bryde’s whale
1) Diet composition
Data from Tamura et al. (2009a) which is based on data collected from JARPNⅡsurvey. 38.35% anchovy(small), 29.94% anchovy (large), 24.49% euphausiids, 4.83% mackerels, and 2.39% others.
2) Biomass
The abundance estimate is from Hakamada et al. (2009). This is based on the JARPN2 sighting survey from2002-2007, and the abundance estimate is 9,344 with CV of about 0.3. Mean body-weight is calculated as 14
tonnes by Tamura et al. (2009a) which is based on data collected from JARPNⅡsurvey. Thus, the biomass is
130,816 tons.
3) P/B or Z
As for M, 0.08 which is used in the comprehensive assessment of NP Bryde’s whales will be used (IWC 2006).
The current F is about 0.016. Thus, Z=M+F=0.10 will be used.
4) Q/B
Data from Tamura et al. (2009a) which is based on data collected from JARPNⅡsurvey will be used. Q/B=4.38(range 3.2-6.3).
5) Catch
50 Bryde’s whales are caught every year, with mean body-weight of 14 tons. Thus, the total catch is 700
tons/year.
Sei whale
1) Diet composition
Data from Tamura et al. (2009a) which is based on data collected from JARPNⅡsurvey. 31.19% anchovy(small), 25.55% anchovy (large), 21.60% euphausiids, 13.15% copepods, 6.88% mackerelss, 1.46% Pacific saury,
0.11% sardine and 0.07% large surface squid.
2) Biomass
The abundance estimate is from Hakamada et al. (2009). This is based on the JARPN2 sighting survey from2002-2007, and the abundance estimate is 7,646 with CV of about 0.3. Mean body-weight is calculated as 20.4
tonnes by Tamura et al. (2009a) which is based on data collected from JARPNⅡ survey. Thus, the biomass is
155,978 tons.
3) P/B or Z
Masaki (1976) estimated the M of sei whales based on the age composition data obtained during 1965 to 1972.
M for female sei whales is estimated to be 0.06 and that for males to be 0.054, which average 0.057. Current F is
0.007, thus Z=0.06 is used.
4) Q/B
Data from Tamura et al. (2009a) which is based on data collected from JARPNⅡsurvey will be used. Q/B=4.16(range 2.9-6.3).
355) Catch
100 sei whales are caught every year, with mean body-weight of 20.4 tons.
tons/year.

Thus, the total catch is 2,040

Other baleen whales
Here, we consider blue whale, fin whale, humpback whale and right whale.
1) Diet composition
Data from Nemoto and Kawamura (1977). It is the average of the diet composition of the four baleen whale
species considered here (blue, fin, humpback and right whales). 65% euphausiids, 25% copepods, 9% fish (herewe assume 4.5% anchovy (large) and 4.5% Pacific saury since these are the main fishes taken by minke whales),and 1% large surface squid.
2) Biomass
There is no estimate of abundance available, thus the following guess estimate is used:
Fin whale 2,000 × 80 tonnes = 160,000 tonnes,
Blue whale 100 × 170 tonnes = 17,000 tonnes,
Humpback whale 200 × 65 tonnes = 13,000 tonnes, totaling 190,000 tonnes.
3) P/B or Z
Since there is no available estimate for these species, we use the same M estimate as assumed for sei whales;
Z=0.057.
4) Q/B
Data from Tamura (2003). Q/B=3.80 (range 2.2-6.3).
5) Catch
There is no catch taken.
Sperm whale
1) Diet composition
Data from Tamura et al. (2009b) which is based on data collected from JARPNⅡsurvey. 86.3% mid-deepwater squid, 3.7% neon-flying squid, 6.4% large surface squid and 3.6% others.
2) Biomass
Hakamada et al. (2009) estimated the abundance in Area7~9 to be 38,370 (CV=0.2) and this estimate is used.
Mean body-weight is calculated as 19 tonnes by Tamura et al. (2009b) which is based on data collected fromJARPNII survey. Thus, the biomass is 729,030 tons.
3) P/B or Z
Based on IWC (1983), M is estimated to be 0.102. F=0.0002, thus Z=0.102.
4) Q/B
Data from Tamura et al. (2009b) which is based on data collected from JARPNⅡ survey will be used.
Q/B=4.20 (range 2.9-6.3).
5) Catch
10 sperm whales are caught every year, with mean body-weight of 19 tons. Thus, the total catch is 190
tons/year.
Baird’s beaked whale
1) Diet composition
Ohizumi et al. (2003) estimated the diet composition of Baird’s beaked whale sampled at Wada local whalingbase on the Pacific coast of central Japan on 1999 (July-August). This results in 0.04% neon-flying squid, 1.3%
small surface squid, 0.86% large surface squid, 2.2% mid-deep water squid and 95.6% others (mainly
Coryphaenoides longifilis).
2) Biomass
Miyashita and Kato (1993) estimated the abundance to be 5,000 (95%CI: 2,500-10,000) based on the 1991-1992
survey. The mean body-weight is estimated to be 14 tons (Kasamatsu 2000) thus, resulting in a biomass estimateof 70, 000 tons (95%CI: 35,000-140,000).
3) P/B or Z
F is estimated to be 0.01. M is assumed to be same as sperm whales (i.e. M=0.1). Thus, Z=0.11.
4) Q/B
Data from Tamura (2003) is used. Q/B=5.53 (range 5.0-6.3).
5) Catch
52 Baird’s beaked whales are caught every year in the Pacific region, with mean body-weight of 14 tons. Thus,
the total catch is 728 tons/year.
36

Short finned pilot whale
Here, we include the northern and southern form north of 30°N.
1) Diet composition
We use the estimate of Gannon et al. (1997) which sampled long-finned pilot whales off the northeastern UnitedStates. Short-finned pilot whales and long-finned pilot whales belongs to the same Genus, and preliminary studyof samples collected at Ayukawa (coastal area in the western North Pacific) show 100% long-finned squid(Konishi pers. commn.). The estimate used is 82% large surface squid, 1% mid-deep water squid, and 17%
others.
2) Biomass
Miyashita (1993) estimated the abundance of the southern form distributed north of 30°N to be 35,000 using
usual line transect formula, based on cruises conducted between 1983 to 1991 during August and September.
The abundance of the northern form is estimated to be 4,300 (CV=0.61) (Anon 1992) based on cruises conducted
between 1982 to 1988, totaling 40,000 whales. The mean body-weight is estimated to be 0.64 tonnes, resulting
in biomass estimate of 25,600 tonnes.
3) P/B or Z
F is estimated to be 0.002. We assumed M to be same as sperm whale (i.e. M=0.1). Thus, Z=0.10.
4) Q/B
Data from Tamura (2003) is used. Q/B=7.42 (range: 6.3-8.9).
5) Catch
Catch of the northern and southern form is 271 whales for 2006, which amounts to 173 tons (Fisheries Agency ofJapan 2008).
Ziphiidae
1) Diet composition
Here, we assume the same diet composition as sperm whale, which is 86.3% mid-deep water squid, 3.7%
neon-flying squid, 6.4% large surface squid and 3.6% others.
2) Biomass
Based on the sighting data obtained from JARPNII survey, sighting rate of ziphiidae is 1.37 times that of minke
whales, and the mean school size (including those of unconfirmed) is 2.09 times larger than that of minke whales.
Assuming that the mean body weight is same for minke whales and ziphiidae, biomass of ziphiidae could be 2.9
times larger than that of minke whales.
3) P/B or Z
Again, since there is no data, we assume that P/B is same as sperm whale (P/B=0.10).
4) Q/B
Data from Tamura (2003) of the cuvier’s beaked whale will be used. Q/B=7.06 (range 6.23-8.23).
5) Catch
There is no catch taken.
Other toothed whale
Here we consider the following seven species: bottlenose dolphin, pantropical spotted dolphin, striped dolphin,
pacific white-sided dolphin, northern-right whale dolphin, risso’s dolphin and dall’s propoise (truei type).
1) Diet composition
We use data from Pauly et al. (1998) and take the biomass weighted average diet composition for the sevenspecies considered here; which results in 59.2% fish, 37.94% cephalopoda, and 3.02% euphausiids. Moreprecisely, we further divide this as 29.6% lanternfish, 14.8% anchovy (large), 18.97% small surface squid, 18.97%
large surface squid, and 14.8% others.
2) Biomass
The abundance estimate of Government of Japan (2002, Appendix 1, Table 1) will be used except for dall’s
propoise. We use Fisheries agency of Japan (2008) for the abundance estimate of dall’s propoise. The total
biomass for the 7 species is 109,911 tons. CV for each abundance estimate is about 0.2, which results in a 95%
CI of 96,514-123,309 tons. Since some of the dalli type dall’s porpoise also migrate to the western north pacificarea, this estimate may be a little underestimate.
3) P/B or Z
Since there is no data, we assume that P/B is same as sperm whale (P/B=0.10).
4) Q/B
Data from Tamura (2003) will be averaged for the seven species. Q/B=11.03 (range 6.3-16.4).
5) Catch
Total catch for these species is about 705 tons for year 2006 (Fisheries Agency of Japan 2008).
37

Northern fur seal
1) Diet composition
Yonezaki (pers. comm.) suggests diet composition of 49.4% lanternfish, 23.9% pollock, 4.2% mackerels, 15.7%
mid-deep water squid and 6.8% others which is based on recent surveys in Area 7.
2) Biomass
The abundance in the western subarctic region (WSA) is unknown. Hunt et al. (2000 Appendix Table 9.11)
estimates the abundance in the western tropical zone (WTZ) to be 190,000 animals. The abundance is derived
from line transect method. The mean body-weight is 28 kg resulting in a biomass estimate of 5,320 tons. The
uncertainty associated with this estimate is unknown.
3) P/B or Z
We use the estimate used by Aydin et al. (2003) for the northern fur seals in the subarctic region of the Pacific.
The estimate is to be 0.235.
4) Q/B
We use the estimate used by Aydin et al. (2003) for the northern fur seals in the subarctic region of the Pacific.
The estimate is to be 39.03. It is estimated from the daily ration and caloric requirement estimates in Hunt et al.
(2000).
5) Catch
There is no catch taken.
Marine birds
1) Diet composition
Average value for the marine birds in the WTZ and WSA calculated in Hunt et al. (2000) is used. This resultsin 16% small surface-squid, 12% euphausiids, neon-flying squid, lanternfish, Pacific saury, mackerels, anchovy(small and large ) and pacific pomfret.
2) Biomass
The density in WSA is 3.8 kg/km2, and that in WTZ is 3.2 kg/km2 (Hunt et al. 2000). JARPNⅡarea overlaps
with both areas, thus we take the mean of these two areas, which results in density estimate of 3.5 kg/km2.
3) P/B or Z
We use the estimate used by Aydin et al. (2003) for the WSA ECOPATH models of 0.1.
4) Q/B
We take the average value of WSA and WTZ in Hunt et al. (2000) which results in an estimate of 54.57.
5) Catch
There is no catch taken.
Albacore
1) Diet composition
Watanabe et al. (2004a) estimated the diet composition of albacore taken in JARPNⅡsurvey area 8 and 9 during
spring to autumn in 2001 and 2002, resulting in an estimate of 43.8% anchovy (large), 36.9% anchovy (small),
4.8% lanternfish, 0.1% mid-deep water squid, 0.3% euphausiids, 6.4% large surface squid, 0.9% small surface
squid, 2% neon-flying squid and 4.9% of others.
2) Biomass
Immature albacore mainly migrate to the JARPNII survey area. Biomass of immature albacore in the northern
pacific area (north of 30ºN and 130ºE to 120ºW) in 2006 is about 330 thousand tons (Fisheries Agency of Japan
2008). Assuming equal density among the distributional areas, biomass in the JARPNⅡsurvey area results in an
estimate of 165 thousand tons.
3) P/B or Z
We assume M=0.3 which is normally used in the assessment of this stock, and F is about 0.11. Thus, we
assume Z=0.41.
4) Q/B
According to Watanabe et al. (2004a), albacore consume 1.08% of its body-weight per day, and the approximate
length staying in the JARPNⅡsurvey area is about 180 days. Thus, we assume Q/B=1.94.
5) Catch
Catch is taken from Anon. (ISC) 2007. The category of “pole-and line, purse seine, troll and others” areconsidered here. Catch in 2005 is about 18,000 tons (According to K. Watanabe (pers. comm.) this estimate may
be a little over-estimate since there is also pole-and line fisheries in the area 25º-35 ºN).
Swordfish
1) Diet composition
According to Watanabe et al. (2004b) and Watanabe (pers. comm.), we assume the following diet composition:3838.2% neon-flying squid, 20.9% large surface squid, 2.1% small surface squid, 6.5% mid-deep water squid, 11.1%
pacific pomfret, 1.12% lanternfish, 0.35% mackerels, 0.4% skipjack tuna and 19.3% others.
2) Biomass
According to Yokawa (pers.comm.), the biomass of swordfish migrating to the JARPNⅡ survey area is about
5,000-33,000 tonnes (here we use the mean of 19,000 tons).
3) P/B or Z
Since there is no data, we assume the same value assumed in Cox et al. (2002a) of 0.60 which is used for the
ECOPATH model in the Central north pacific, which is based on 1990-1998 average estimated fromsingle-species model assessments (Cox et al. 2002b).
4) Q/B
According to Watanabe (unpublished), swordfish consume 1.14% of its body-weight per day. The approximate
length staying in the JARPNⅡsurvey area is about 180 days. Thus, we assume Q/B=2.05.
5) Catch
Catch in the JARPNⅡ area is unknown, but in the North West Pacific Area (north of the equator and west of thedate-line), the total catch in 2005 is 6,972 tons (Fisheries agency of Japan 2008). When estimating the biomass,
it is assumed that 1/3-1/10 of the total biomass in the North West Pacific area migrate to the JARPNⅡarea.
Thus, assuming that the same percentage of the catch is taken in the JARPNⅡarea result in a catch of 697-2,324
tons (average 1,511 tons).
Skipjack tuna1) Diet composition
According to Watanabe (unpublished), based on the survey in 2003 and 2004 in the JARPNⅡarea, the suggested
diet composition is 42% anchovy (small), 40% anchovy (large), 9% euphausiids, 1% small surface squid, 4%large surface squid, 0.5% lanternfish, 0.2% mackerels and 3.3% others.
2) Biomass
According to Langley et al. (2005), average biomass from 2000 to 2004 is about 220,000 tons in Region 2 (northof 25ºN, east of 140 ºE to 165 ºE). This estimate may be a little overestimate since it includes the area 25 ºN-35 ºN.
P/B or Z3) P/B or Z
According to Fisheries Agency of Japan (2008), M of the mature fish is about 0.4, and F is about 0.5. Thus, we
assume Z=0.9.
4) Q/B
According to Nakatsuka et al. (2006) based on the specimens captured in the Kuroshio-Oyashio transition regionin May and June 2006, skipjack tuna consume about 3.6% of its body-weight/day. Thus, if we assume thisspecies to spend 180 days in the JARPNⅡsurvey area, results in an estimate of about Q/B=6.0.
5) Catch
According to Fisheries Agency of Japan (2008), average catch from 2004-2007 is about 110,000 tons.
Blueshark
1) Diet composition
According to Kubodera et al. (2007) and Watanabe (unpublished), the suggested diet composition is 8.7%
lanternfish, 4.5% pacific pomfret, 0.75% anchovy (large), 47.6% mid-deep water squid,10.2% large surface squid,
4% small surface squid, 5.8% neon-flying squid, 0.3% albacore and 18.3% others.
2) Biomass
According to Kleiber et al. (2001), biomass estimate of this species in the North Pacific area range from about
400,000-1,200,000 tons. By multiplying this value with the percentage of JARPNⅡ area results in an estimateof 20,000-70,000 tons (average 45,000 tons).
3) P/B or Z
Since there is no data, we assume the same value assumed in Cox et al. (2002a) of 0.39 (average of mature andimmature animals) which is used for the ECOPATH model in the Central north pacific, which is based on
single-species model assessments.
4) Q/B
Since there is no data, we assume the same value assumed in Cox et al. (2002a) of 3.1 (average of mature andimmature animals) which is used for the Central north pacific ECOPATH model. This results in growthefficiency of 13%, which is in the plausible range of 10-30%.
5) Catch
According to Fisheries agency of Japan (2008), catch in 2006 in the JARPNⅡ area is about 11,453 tons.
39Salmonshark1) Diet composition
According to Kubodera et al. (2007) and Watanabe (unpublished), the suggested diet composition is 14.3%
mid-deep water squid, 79.3% large surface squid, 2.8% small surface squid and 3.6% others.
2) Biomass
Since there is no suitable data, we multiply the biomass of blue shark with the catch ratio of salmonshark andblue shark (0.28). This results in an estimate of 5,600-19,600 tons.
3) P/B or Z
Since there is no actual estimate, we assume the same value as for blueshark of P/B=0.39.
4) Q/B
According to Wetherbee et al. (1990), daily consumption of sharks is 0.4-3.2% of its body-weight. Assumingthat salmon shark feed every day, Q/B range from 1.5 to 11.7. We use the mean of Q/B=5.1.
5) Catch
According to Fisheries agency of Japan (2008), catch in 2006 is 3,881 tons.
Lanternfish
1) Diet composition
According to Moku et al. (2000) based on the survey data obtained during the summers of 1994 to 1996 in thesubarctic and transitional waters of the western North Pacific, average diet composition of Diaphus theta,
Stenobrachius leucopsarus, and Stenobranchius nannochir is 36% copepods eaten by whales, 18% other copepods,
24% euphausiids, and 22% others.
2) Biomass
According to Brodeur and Yamamura (2005), biomass (g/m2) range from 2.8-18.5 g/m2. The average density
results in a biomass estimate of 30,000,000 tons. This estimate includes Bathylagidae, Gonostomatidae, thus we
use the half of this estimate; 15,000,000 tons.
3) P/B or Z
Moku et al. (2000) based on the survey data obtained during the summers of 1994 to 1996 in the subarctic andtransitional waters of the western North Pacific estimated Q/B=13.35. Thus, using the relationship of g (growthefficiency) =0.1-0.3, results in an estimate of P/B=1.34-4.01 (mean is 2.68). Here we use P/B=2.68.
4) Q/B
According to Moku et al. (2000), the daily consumption of D. theta is 6.3%, S. leucopsarus is 3.0%, and S.
nannochir is 0.33% of its body weight. For some sub-tropical species, the daily consumption is around 5% of its
body-weight. The average of these becomes 3.7%. Assuming that lanternfish feed year around results in an
estimate of Q/B=13.35.
5) Catch
There is no catch taken.
Mackerels
1) Diet composition
We assume same value as in Okamura et al. (2001), except that we assume that no sardine is eaten, since thebiomass of this species has declined dramatically in recent years. This results in a diet composition of 50%
euphausiids, 30% copepods, 10% anchovy (3% small and 7% large), 2% lanternfish, Pacific saury, 1% large
surface squid, small surface squid , neon-flying squid and mid-deep-water squid.
2) Biomass
According to Fisheries Agency of Japan (2008), biomass in 2006 is around 555,000 tons for common mackerels,
and 459,000 tons for southern mackerels, totaling 1,015,000 tons.
3) P/B or Z
M is around 0.4 (Honma 1987), and average F is 0.63. Thus, average Z=1.03.
4) Q/B
We use the relationship of g =0.1-0.3. This result in an estimate of Q/B=3.43-10.3. Here, we assume themean of Q/B=6.87.
5) Catch
Catch in 2006 is 428,384 tons (Fisheries agency of Japan 2008).
Pacific pomfret
1) Diet composition
According to Watanabe et al. (2003) and (2006), which is based on survey samples collected in April and May
2000 in the transition zone of the central North Pacific, and samples collected in July 2002 in the transitional
domain and subarctic region of the central North Pacific, the following diet composition is suggested: 0.3%40euphausiids, 22.9% large surface squid, 37.2% small surface squid, 3.9% neon-flying squid, 2.3% mid-deep watersquid, 3.3% lanternfish, 3.1% Pacific saury, 1.4% anchovy (small) and 25.7% others.
2) Biomass
Although there is no direct estimate of the biomass of pacific pomfret, data from drift net survey in this areafrom 2000-2006 (Hokkaido University, 2000-2006) suggest that, the biomass of pacific pomfret is at least 1.4
times that of neon-flying squid. Thus, here we assume 1.4 times the biomass of the neon-flying suid.
3) P/B or Z
Since there is no suitable information for this particular area, we use the estimate used by Aydin et al. (2003) of
0.75 for the pacific pomfret in the western subarctic region of the Pacific.
4) Q/B
Since there is no suitable information for this particular area, we use the estimate used by Aydin et al. (2003) of
3.75 for the pacific pomfret in the western subarctic region of the Pacific. This estimate is derived fromassuming growth efficiency of 0.2.
5) Catch
There seems to be some by-catch but there is lack of reasonable estimate, thus here we provisionally assume that
1% of the total biomass is caught by by-catch etc.
Japanese sardine
1) Diet composition
We use the same estimate as in Okamura et al. (2001), of 70% phytoplankton, and 30% copepoda.
2) Biomass
According to Fisheries agency of Japan (2008), the biomass in 2006 is estimated to be 132,000 tons.
3) P/B or Z
According to Fisheries agency of Japan (2008), M is 0.4 and F=0.37, thus we assume Z=0.77.
4) Q/B
Since there is no suitable information, we use the relationship g=0.1-0.3, resulting in an estimate of
Q/B=2.57-7.7. Here we assume Q/B=5.14
5) Catch
The catch in 2006 is 48,571 tons.
Japanese anchovy1) Diet composition
We use the same estimate as in Okamura et al. (2001), of 20% euphausiids, and 80% copepoda for both small
and large anchovy.
2) Biomass
According to Fisheries Agency of Japan (2008), the biomass estimate of anchovies larger than 5cm is 4,800,000
tons. If we assume that the biomass of those less than 5cm is half the biomass of the 0-age classes (<8cm),
biomass of those between 5-8cm is about 11-27% of those biomass larger than 5cm, according to the stock
assessment of anchovy between 200-2005 in Fisheries agency of Japan (2008). Thus, the biomass between
5-8cm would be around 530,000 – 1,300,000 tons. Thus, the biomass of the large component of anchovy would
be around 3,500,000-4,300,000 tons. Here we assume the mean of 3900,000 tons for the large component.
3) P/B or Z
According to Fisheries agency of Japan (2008), M is around 1.5 and F=0.1 for the large component of anchovy,thus we use Z=1.6 for the large component. For the small component, M =1 is assumed in Fisheries agency ofJapan (2008), but it notes that the actual M should be higher when fishing of the small component is considered.
Thus, here we use the same Z as the large component.
4) Q/B
Although there is no suitable data in JARPNⅡarea, Tudela and Palomera (1995)estimated Q/B for European
anchovy to be 3.9% of its body-weight/day. Thus assuming that this species feed year around, Q/B=14.04.
5) Catch
According to Fisheries agency of Japan (2008), the average catch (2000-2005) for the small component is about
5,000 tons, and the average catch for the larger component is about 300,000 tons.
Pacific saury
1) Diet composition
According to Sugisaki and Kurita (2004), which is based on seven cruises conducted from 1999 to 2002 in the
JARPNⅡsurvey area in different seasons, 23.8% is copepods eaten by whales, 50.1% is euphausiids, and 26.1%
is others.
412) Biomass
According to Fisheries agency of Japan (2008), the biomass in 2007 is estimated to be 4,400,000 tons.
3) P/B or Z
According to Fisheries agency of Japan (2008), M=0.64 is used for the assessment of this species. Recent F is0.1, thus Z=0.74 is used.
4) Q/B
According to Sugisaki and Kurita (2004), in Autumn-Winter, Pacific saury consumes about 4% of its
body-weight. When in spring-summer, Pacific saury consumes 7% of its body-weight. Thus, we assume
Q/B=10.
5) Catch
Catch in 2006 is 392,148 tonnes, and the discarding is about 20,500 tonnes.
Neon-flying squid
1) Diet composition
According to Watanabe et al. (2004c) and Watanabe (unpublished), which examined the feeding habits of thisspecies for different years, seasons in the transitional waters of the central North Pacific in the 1990s and 2000s,
the following diet composition is suggested: 0.1% euphausiids,16.4% large surface squid, 25.4% small surface
squid, 1.4% neon-flying squid, 4.1% mid-deep water squid, 28.4% lantern fish, 14% anchovy (large), 0.9%
anchovy (small) and 9.3% others.
2) Biomass
According to Fisheries agency of Japan (2008), the biomass is around 140,000-400,000 tons (mean is 270,000
tons).
3) P/B or Z
M of the autumn cohort of neon-flying squid is estimated to be 0.089 per 10-day period by Ichii et al. (2006),
which amounts to M=3.25/year. F is assumed to be 0.37, thus Z is assumed to be 3.62.
4) Q/B
According to Watanabe et al. (2004c), neon-flying squid consume 6.0% of its body-weight/day. Thus, assumingthat they spend 180days feeding in this area, Q/B=10.8.
5) Catch
According to Ichii (pers. comm.), the average catch from 2000-2004 is about 100,000 tons (catch by China isincreasing; almost 5 times the catch of Japan).
Large surface squid
Here we consider the following species: Japanese common squid, clubhook squid, Eight-armed squid, Kagi-ika,
Sujiika which there DML is larger than 15cm.
1) Diet composition
Referring to the diet composition of neon-flying squid (Watanabe et al. 2004), we assume 46.4% euphaussids,
8.03% small surface squid, 2.51% anchovy (small), 6.27% lanternfish, and 36.8% others (mainly amphipods).
2) Biomass
The biomass of Japanese common squid (winter population) is estimated to be around 800,000 tons. Although
there is no direct estimate of the biomass for other large surface squids, data from drift net survey in this area from
2000-2006 (Hokkaido University, 2000-2006) suggest that, the biomass of other large surface squid is at least 0.83
times that of neon-flying squid, which amounts to 120,000 – 330,000 (mean is 220,000) tons. This survey did
not sample Kagi-ika, thus this estimate may be an underestimate. Nevertheless, the estimate is about 1,000,000
tons.
3) P/B or Z
M of Japanese common squid is often set to 0.1/month (Fisheries agency of Japan 2008), which amounts to
M=1.2/year. F=0.135, thus Z for Japanese common squid may be 1.34. Considering the Z value of neon-flying
squid (Z=3.62), we take the mean of these two estimate which results in an estimate of Z=2.48.
4) Q/B
We use the same value used for neon-flying squid (Q/B=10.8)
5) Catch
We use the catch of Japanese common squid of 135,000 tons (Fisheries agency of Japan (2008).
Small surface squid
Here we consider species of Gonatidae (eight-armed squid is excluded) which there DML is mainly less than
15cm.
1) Diet composition
According to Uchikawa (unpublished), small surface squid consume 77.8% other copepods, 5.56% euphausiids,42

and 16.7% others.
2) Biomass
There is no estimate of biomass. Thus we let the model to estimate assuming EE=0.9.
3) P/B or Z
Since the body size of small surface squid is smaller than the large surface squid, we assume higher M, such as
M=0.1 per 10-day period. This amounts to M=3.65/year. Thus, here we assume Z=3.65.
4) Q/B
We use the same value used for the large surface squid and neon-flying squid, which is Q/B=10.8.
5) Catch
No catch is taken.
Mid-deep water squid
1) Diet composition
We use the same as in Okamura et al. (2001), which assume 60% euphausiids, and 40% copepods.
2) Biomass
Since there is no suitable information, we let the model to estimate the biomass assuming EE=0.9.
3) P/B or Z
Since there is no suitable information for this particular area, we use the estimate used by Aydin et al. (2003) of
2.56 for the squids in the western subarctic region of the Pacific.
4) Q/B
We use the same value used for neon-flying squid (Q/B=10.8).
5) Catch
There is no catch taken.
Phyto-plankton
1) Biomass
Since there is no suitable information, we use the estimate of Cox et al. (2002a) of 26.58 t/km2 which is used for
the Central north pacific ECOPATH model which was estimated in the model. Also, Saito (pers.commn)
suggests an order of 15- 40 t/km2 (average 27.5 t/km2) for the modeled region.
2) P/B or Z
We use the estimate of 85.96 of Aydin et al. (2003).
Euphausiids
1) Diet composition
Based on Nakagawa et al. (2004) and Taki (un published), the following diet composition is assumed: 9%
copepods eaten by whales, 9% copepods not eaten by whales, 12% phytoplankton and 70% detritus.
2) Biomass
We use the estimate used by Aydin et al. (2003) for the ECOPATH model in the western subarctic region, which
is 40.67t/km2.
3) P/B or Z
We use the estimate used by Aydin et al. (2003) for the ECOPATH model in the western subarctic region, whichis 2.555.
4) Q/B
Since there is no suitable information for this particular area, we use the estimate used by Aydin et al. (2003) of
12.05 for the euphausiids in the western subarctic region of the Pacific.
Copepods eaten by whales
1) Diet composition
According to Kobari et al. (2003) which is based on survey in 1996-1997 in Area 7 of JARPNⅡsurvey area, we
assume 26% phytoplankton and 74% detritus.
2) Biomass
Use the estimate of Murase (pers. commn.) of 59,100,000 tons.
3) P/B or Z
According to Kobari et al. (2003) which is based on survey in 1996-1997 in Area 7 of JARPNⅡsurvey area,
P/B= 5.0.
4) Q/B
Based on Murase (pers. commn.), we assume Q/B=10.0.

43Other copepods
Since there is no suitable information, we assume the same value as “copepods eaten by whales”.
Detritus
To estimate the biomass of detritus, we use the same method used in Okamura et al. (2001), which multiplies thebiomass of phytoplankton by 5 (Tsujita 1982).

44

Appendix C: Parameter settings of the functional response in Ecosim and its assumptions
Name FRMax.
rel.
feeding time

Feedingtime
adjust rate [0,1]Type 1
Type 2Type 3
Type 3+other M
Type 3+switch
Default

2
2
2
2
2
2

0
0
0.5
0.5
0.5
0.5

Fractionof“other”
mortality sens.
To changes in
feeding time
0
0
0
1
0
1

Predator effecton feeding [0,1]Density-dep.
Catchability
Qmax/Q0 [>=1]

QBmax/QB0(for
handlingtime) [>1]

Switchingpowerparameter [0,2]

0
0
0.5
0.5
0.5
0

1
1
1
1
1
1

1000
2
2
2
2
1000

0
0
0
0
2
0

Type 1 functional relationship assumes that the feeding time is constant regardless of the density of its prey or predators, and there is no density dependent mortality or
limit in handling time. Consumption rate is proportional to available prey biomass.
Type 2 functional relationship is similar to Type 1 and assumes that the feeding time is constant, though there is a limit in handling time.
Type 3 functional relationship assumes that all the species considered in the model adjust their feeding time in relation to their food and predator density (risk sensitive
behaviour), and there is a limit in handling time.
Type 3 + other M functional relationship adds the density dependent mortality feature to the Type 3 functional relationship assumed above.
Type 3 + switch functional relationship adds the behaviour of taking disproportionately more of the thing as it becomes more abundant.
The default functional relationship assumes that all the species considered in the model adjust their feeding time in relation to their food density, and mortality rate of thespecies change in relation to feeding time (i.e. density dependent mortality), and there is no limit in handling time.

ADDENDUM
Has obtaining further data on diet composition of whales improved the precision of
the estimate of % increase in catch of Japan’s fisheries resources calculated by theEwE model?
The average diet composition for each JARPNII survey year including the feasibility study period (8 in all) is
treated as a sampling unit. First we bootstrap 10 times over the first 5 years (3 years for sei whales) to get an
uncertainty distribution (i.e. choose successive sets of 5 years’ data, choosing 5 times at random with replacementfrom the averages for each year, and then averaging over the 5). Next we conduct exactly the same exercise but
for 8 years. For each bootstrap diet composition data, we calculate the % increase in catch of some Japanesefisheries resource. From such a calculation, we obtain the average % increase in catch of their Japanese fisheries
resource and the standard deviation of the bootstrap distribution which is the standard error associated with thisaverage % increase estimate. The objective is to see if the standard error has improved by accumulating 8 yearsof diet composition data, compared to five years. We repeat this whole procedure 5 times to examine how
precise the estimate of such standard error is.
Results for minke whales
Table a-1 shows bootstrap % increase in catch of Pacific saury between no catch and taking 4% of minke whales
for the coming 50 years for cases of 5 years accumulation of diet composition data and 8 years of such. Table
a-2 shows the mean and average standard error of the % increase in catch of Pacific saury over the five trials.
The results indicate that estimated % increase in catch of Pacific saury decreases from 1.697% (SE=0.330) to
1.470% (SE=0.289) with further 3 years of accumulation of the diet composition data. The standard error of thisestimate has improved by about 10%.
Table a-1. Bootstrap % increase in catch of Pacific saury between no catch and taking 4% of minke whales for the
coming 50 years (fishing effort for the other harvested species is kept as current). “5 years” bootstrap dietcomposition for the first five years of JARPNII surveys, and “8 years” bootstrap that for the whole JARPNII
survey period. The whole procedure is repeated for five times with different realizations of sampling withreplacement (Trial 1 to Trial 5).
Pacific saury
boot1
boot2
boot3
boot4
boot5
boot6
boot7
boot8
boot9
boot10
Mean
SE

Trial 3
Trial 1
Trial 2
Trial 4
Trial 55years
8years
5years
8years
5years
8years
5years
8years
5years
8years
1.729
1.794
2.055
1.729
1.859
1.34
1.859
1.598
1.859
1.924
1.990
1.729
1.794
1.34
1.082
1.598
1.276
1.405
1.924
1.146
1.729
1.340
2.382
1.794
1.794
1.082
1.99
1.082
1.534
1.924
1.794
1.018
2.055
1.34
2.186
1.729
1.47
1.405
1.276
1.663
1.598
1.276
1.924
1.276
2.382
1.924
1.405
1.924
1.924
1.729
1.470
1.598
1.405
1.34
1.794
0.954
1.211
0.762
1.598
1.276
2.121
1.598
1.729
1.859
1.340
1.340
1.082
1.146
2.055
1.211
1.405
1.598
2.186
1.082
1.276
1.34
0.954
1.211
1.729
1.663
1.729
1.470
1.859
1.794
1.276
1.729
1.598
1.47
1.794
1.729
1.211
1.470
1.794
1.211
1.276
1.405
2.382
1.73
1.729
1.405
1.678
1.489
1.918
1.477
1.627
1.444
1.523
1.373
1.742
1.567
0.270
0.230
0.270
0.286
0.440
0.302
0.443
0.338
0.227
0.287

Table a-2. Mean and standard error (SE) of the % increase in catch for Pacific saury for Trials 1 to 5 shown on
Table a-1. Standard deviation and standard error of the SE is also shown.

Pacific saury
Mean % increase in catch
Mean of the SEs for the 5 trials
Standard deviation of the SE
Precision (Standard error) of the SE

5 years
1.697
0.330
0.103
0.046

8years
1.470
0.289
0.039
0.017

Results for sei whales
Table a-3 shows bootstrap % increase in catch of anchovy (>=8cm) between no catch and taking 4% of sei whales
for the coming 50 years for cases of 3 years accumulation of diet composition data and 6 years of such 1. Table
a-4 shows the mean and average standard error of the % increase in catch of anchovy (>=8cm) over the five trials.
The results indicate that estimated % increase in catch of anchovy (>=8cm) decreases from 3.654% (SD=0.696) to
2.708% (SD=0.766) with further 3 years of accumulation of the diet composition data. However, the standard
error of this estimate has not improved. This could be because the amount of anchovy consumed by sei whales
varies substantially from year to year – note the lesser precision of the SE estimates in Table a-4 compared tothose in Table a-2.
Table a-3. Bootstrap % increase in catch of anchovy (>=8cm) between no catch and taking 4% of sei whales forthe coming 50 years (fishing effort for the other harvested species is kept as current). “3 years” bootstrap dietcomposition for the first three years of JARPNII surveys on sei whales, and “6 years” bootstrap that for the whole
JARPNII survey period. The whole procedure is repeated for five times with different realizations of samplingwith replacement (Trial 1 to Trial 5).
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5anchovy (>=8cm) 3 years
6 years
3 years
6 years
3 years
6 years
3 years
6 years
3 years
6 years
boot1
2.728
3.235
2.149
2.077
3.672
3.235
4.549
3.381
4.549
4.329
boot2
3.963
3.235
3.672
3.672
4.549
2.511
3.672
3.963
3.672
2.728
boot3
3.672
1.861
2.728
2.582
4.549
2.728
3.963
1.933
2.149
2.945
boot4
3.017
3.235
3.963
2.945
3.672
3.017
3.672
3.672
4.549
3.235
boot5
3.963
3.017
4.182
2.945
4.841
3.453
3.453
3.235
2.728
1.790
boot6
2.728
1.430
3.672
1.861
3.017
2.511
4.182
0.356
4.182
1.358
boot7
2.728
1.574
4.549
2.945
2.728
3.453
4.182
1.790
3.453
2.800
boot8
3.672
2.366
3.017
2.149
3.017
2.149
4.549
2.511
4.182
3.089
boot9
3.453
4.182
2.728
2.800
3.963
3.381
3.672
2.728
4.841
1.790
boot10
4.549
2.294
3.672
2.582
3.017
3.525
3.017
2.294
3.672
2.511
Mean
3.447
2.643
3.433
2.656
3.703
2.996
3.891
2.586
3.798
2.658
SE
0.631
0.881
0.751
0.531
0.757
0.490
0.488
1.068
0.852
0.859

Table a-4. Mean and standard error (SE) of the % increase in catch for anchovy (>=8cm) for Trials 1 to 5 shownon Table a-3. Standard deviation and standard error of the SE is also shown.
anchovy (>=8cm)
3 years
6yearsMean % increase in catch
3.654
2.708
Mean of the SEs for the 5 trials
0.696
0.766
Standard deviation of the SE
0.140
0.247
Precision (Standard error) of the SE
0.063
0.111

1

For sei whales there was no feasibility study period thus the sampling units total 6 in all.
47

Results for Bryde’s whales
Table a-5 shows bootstrap % increase in catch of anchovy (>=8cm) and mackerel between no catch and taking 4%of Bryde’s whales for the coming 50 years for cases of 5 years accumulation of diet composition data and 8 yearsof such. Table a-6 shows the mean and average standard error of the % increase in catch of anchovy (>=8cm)
and mackerels over the five trials. The results indicate that estimated % increase in catch of anchovy (>=8cm)
increases from 3.238% (SD=0.828) to 3.305% (SD=0.584), and that of mackerel decreases from 1.722%(SD=0.997) to 0.991% (SD=0.584) with further 3 years of accumulation of the diet composition data. The
standard errors of these estimates have improved by about 30% for anchovy (>=8cm) and 40% for mackerel.
Table a-5 Bootstrap % increase in catch of (a) anchovy (>=8cm) and (b) mackerels between no catch and taking
4% of Bryde’s whales for the coming 50 years (fishing effort for the other harvested species is kept as current).
“5 years” bootstrap diet composition for the first five years of JARPNII surveys on Bryde’s whales, and “8 years”
bootstrap that for the whole JARPNII survey period. The whole procedure is repeated for five times with different
realizations of sampling with replacement (Trial 1 to Trial 5).
(a) anchovy (>=8cm)
Trial 1
Trial 2
Trial 4
Trial 5
Trial 3anchovy (>=8cm) 5years
8years
5years
8years
5years
8years
5years
8years
5years
8years
boot1
3.219
3.940
3.147
3.794
2.214
4.011
4.444
1.856
3.076
3.508
boot2
4.444
3.722
2.645
2.859
2.788
3.219
2.357
2.859
2.286
3.004
boot3
3.940
3.651
2.357
4.083
3.219
2.573
1.284
3.219
3.579
3.722
boot4
4.083
3.076
2.286
1.927
3.579
2.716
1.784
3.651
4.588
3.722
boot5
3.940
3.651
3.436
2.500
3.940
3.076
3.940
2.357
3.004
4.083
boot6
4.735
3.147
2.788
3.868
2.859
2.645
3.651
2.716
4.444
3.651
boot7
0.927
4.155
3.076
2.859
1.927
3.940
3.579
2.357
2.286
3.219
boot8
5.240
3.076
3.436
3.794
2.716
3.579
2.716
3.147
4.083
4.444
boot9
3.219
2.573
3.508
4.444
2.931
3.076
3.076
3.436
4.083
3.508
boot10
3.579
3.076
4.083
3.219
2.716
3.651
3.219
3.940
3.436
2.931
Mean
3.733
3.407
3.076
3.335
2.889
3.249
3.005
2.954
3.487
3.579
SE
1.174
0.489
0.565
0.792
0.591
0.527
0.983
0.648
0.826
0.464

(b) mackerels
mackerels
boot1
boot2
boot3
boot4
boot5
boot6
boot7
boot8
boot9
boot10
Mean
SE

Trial 1
Trial 2
Trial 3
Trial 4
Trial 55years
8years
5years
8years
5years
8years
5years
8years
5years
8years
3.022
2.192
3.571
1.370
4.121
0.546
1.370
2.747
0.546
1.096
1.370
1.370
1.096
1.096
1.918
0.273
2.192
1.096
1.370
1.096
0.546
0.546
2.192
0.273
1.918
1.644
1.644
1.644
1.918
0.273
1.370
2.192
1.370
1.918
1.918
1.918
2.192
0.000
1.918
0.546
0.546
1.644
1.370
0.546
0.546
1.096
1.644
1.370
1.096
0.546
1.370
1.096
3.846
1.096
1.918
1.370
4.683
0.546
1.370
1.096
3.022
1.096
0.546
0.273
3.571
0.273
0.546
0.273
2.192
1.096
1.096
1.096
1.370
0.273
0.546
0.273
0.546
1.096
1.370
0.546
1.918
0.546
2.747
0.273
3.022
0.546
0.546
1.096
1.370
0.273
1.918
1.918
1.370
1.096
0.546
1.096
0.546
1.096
1.370
1.096
1.618
1.370
1.948
0.821
2.002
0.904
1.591
1.096
1.452
0.766
0.874
0.606
1.104
0.578
1.264
0.607
1.278
0.766
0.467
0.361

Table a-6. Mean and standard error (SE) of the % increase in catch for (a) anchovy (>=8cm) and (b) mackerels for
Trials 1 to 5 shown on Table a-5. Standard deviation and standard error of the SE is also shown.
(a) anchovy (>=8cm)
anchovy (>=8cm)
Mean % increase in catch
Mean of the SEs for the 5 trials
Standard deviation of the SE
Precision (Standard error) of the SE

5 years
3.238
0.828
0.260
0.116

8years
3.305
0.584
0.136
0.061

5 years
1.722
0.997
0.338
0.151

8years
0.991
0.584
0.145
0.065

(b) mackerels

mackerels
Mean % increase in catch
Mean of the SEs for the 5 trials
Standard deviation of the SE
Precision (Standard error) of the SE

48

Conclusion
For minke and Bryde’s whales, obtaining further diet composition data has improved the precision of the estimate
of % increase in catch of Japan’s fisheries resources (e.g. Pacific saury, anchovy, mackerels) calculated by theEwE model. The results indicate that continuation of the JARPN2 survey will contribute by further improving
the precision of the effect of whaling on Japan’s fisheries resources.

49