Technical Reports of the Institute of Cetacean Research (2018)

pp. 14–19

Technical Report (not peer reviewed)

An overview of the Institute of Cetacean Research work on ecosystemmodeling in the Antarctic and western North Paciﬁc
Takashi Hakamada* and Tsutomu Tamura
Institute of Cetacean Research, 4–5 Toyomi-cho, Chuo-ku, Tokyo 104–0055, Japan*Contact e-mail: hakamada@cetacean.jp

ABSTRACTThe development of ecosystem models has been an important research activity organized and promoted by
the Institute of Cetacean Research (ICR). This paper presents an overview of the objectives and progress madein the research on ecosystem modeling in the Antarctic and western North Paciﬁc. Whale abundance, preycomposition and prey consumption of whales have been some of the input data to the models, which wereobtained by ICR surveys. Several future research needs to progress the ecosystem modeling work have been
identiﬁed.

INTRODUCTION
The development of ecosystem models has been an
important research activity of the Institute of Cetacean
Research (ICR) in its whale research programs in the Antarctic and western North Paciﬁc. What is understood byʻecosystem modelsʼ? An ecosystem model is an abstract,usually mathematical representation of an ecologicalsystem, which is studied to better understand the realsystem (Hall and Day, 1990). Ecological relationships,
such as the relation between predators and preys, arederived using data gathered from the ﬁeld, and these are
combined to form ecosystem models. These model systems are studied in order to make predictions about the
dynamics of the real system.
Ecosystem models have applications in a wide variety
of disciplines including natural resource management.
Morishita (2008) provided a good summary of the various
interpretations of the ecosystem approach for ﬁsheries
management, one of them being the evolution from single-species management to multi-species management.
According to the FAO home page (Fisheries and Aquaculture Department), ecosystem models for ﬁsheries
management purposes have ranged in complexity fromenhanced single-species models to fully articulated food
web models including physiological, spatial and environmental processes.
Objectives of ecosystem modeling studies are diﬀerent according to the kind of model used. They could be,for example, 1) understanding structure, features andinteractions of an ecosystem, 2) identifying major causes
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of changes in an ecosystem, 3) prediction of population dynamics for speciﬁc species, 4) assessment of the
impact of ﬁsheries and predation on an ecosystem, 5)
providing some scientiﬁc advice on management of astock or stocks.
Plagányi (2007) reviewed ecosystem modeling studies
and grouped the models into two groups: 1) whole ecosystem models, which are models taking into account all
trophic levels. This includes Ecopath with Ecosim (EwE)(Christensen et al., 2005); and 2) Minimum Realistic
Models (MRM), which are models restricted to represent
a limited number of species most likely to have important interactions with a target species of interest. This
includes MULTSPEC (Bogstad et al., 1997; Tjelmeland andBogstad, 1998) and BORMICON (Stefansson and Palsson, 1998).
This paper summarizes the studies on ecosystem modeling organized and promoted by the ICR in the Antarctic
and western North Paciﬁc Ocean.

ECOSYSTEM MODELING IN THE ANTARCTIC
BackgroundThe ecological background for the ecosystem modelingin the Antarctic is provided by Fujise and Pastene (thisissue). There are some hypotheses related to ecosystemchanges in the Antarctic. The ﬁrst is referred to as theʻkrill surplusʼ hypothesis. After the depletion of Antarctic
blue (Balaenoptera musculus intermedia), ﬁn (B. physalus) and humpback (Megaptera novaeangliae) whalesat the middle of the past century there was some 150
million tons of krill surplus. This surplus became available

An overview of the Institute of Cetacean Research work on ecosystem modeling in the Antarctic and western North Pacificfor other krill-eater species of penguins, seals and smallerwhales (e.g. Antarctic minke whale, B. bonaerensis)(Laws, 1977). The krill surplus implied better nutritional conditions in those species, which was reﬂected inchanges in some demographic parameters. For example
from 1940 to 1970, recruitment had been increasing for
Antarctic minke whales (Punt et al., 2014) reﬂecting better nutritional conditions for the species at the middle ofthe past century.
The second hypothesis is that the population dynamics
of Antarctic species is aﬀected by environmental changes.
A decrease in sea-ice cover by the middle of the twentyﬁrst century due to global warming has been predictedby several studies (Levitus et al., 2000; de la Mare, 1997).
Warming of the Southern Ocean appears to be occurring
faster than the warming of other oceans of the world(Gille, 2002).
It was therefore necessary to investigate and quantify
the interactions among krill predators such as some baleen whale species, and to examine the dynamics of the
Antarctic marine ecosystem. This would enable investigation of what has happened in the past and to predict
what is going to happen in the future in the context of
ecosystems in the Antarctic (GOJ, 2015). For this aim,
ecosystem modeling has been used.
One of the main objective of the ecosystem modeling study in the Antarctic was to determine whether
predator-prey interactions alone can broadly explain observed population trends without the need for recourse
to environmental change hypotheses (Mori and Butterworth, 2006).
MRM Models in the Antarctic
Objectives
The objective of the study by Mori and Butterworth(2006) was to determine whether predator–prey interactions alone can broadly explain the observed population
trends of four large baleen whale species, Antarctic blue,
ﬁn, humpback and Antarctic minke whales; two seal
species, Antarctic fur seal (Arctocephalus gazelle) andcrabeater seal (Lobodon carcinophagus); and Antarctic

Figure 1.

krill (Euphausia superba), without the need for recourse
to environmental change hypotheses.
Ecosystem model
Population dynamics models were developed to describe
predator–prey interactions among the four large baleen
whale species, the two seals species and krill (Figure 1).
Functional responses are generally classiﬁed into three
types, which are called Type I, II, and III (Holling, 1959).
In the model, two types of functional responses wereconsidered, Type II, which assumes that consumption
rate decreases as prey abundance increases and that prey
consumption is limited, and Type III, which assumes that
consumption rate increases as prey abundance increases
when prey abundance is lower than a threshold of prey
abundance, and it decreases as prey abundance increases when prey abundance is higher than the threshold(Holling, 1959). Mori and Butterworth (2006) ﬁtted theabundance estimates of these species to estimate parameters/coeﬃcients in the models by maximum likelihood
method.
Research area and data used
The predator-prey model was constructed in two sectorsof the Antarctic. One in the Atlantic/Indian sector (eastof 60°W and west of 130°E) and the other in the Paciﬁc
sector (east of 130°E and west of 60°W). Historical catch
data for all species considered in the model were used.
Abundance estimates of krill predators for the whole sectors were also used. Abundance trend estimates for blue,
humpback, Antarctic minke whales and crabeater seal
were made for a part of each sector, and were based on
independent data sources.
Results
The main results of Mori and Butterworth (2006) were
the following:(i) species interaction eﬀects alone can explain observed predator abundance trends;(ii) it is necessary to consider species other than baleen whales and krill, to explain observed trends—

Food web in the Antarctic modeled in Mori and Butterworth (2006) and Moosa (2017).
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Technical Reports of the Institute of Cetacean Research (2018)crabeater seals seemingly play an important roleand constitute a particular priority for improved
abundance and trend information;(iii) the Atlantic/Indian Ocean sector shows majorchanges in species abundances, in contrast to the
Paciﬁc Ocean sector, which is much more stable;(iv) baleen whales have to be able to achieve relatively
high growth rates to explain observed trends; and(v) Laws (1977) estimate of some 150 million tons
for the krill surplus may be too high because his
calculations omitted consideration of densitydependent eﬀects in feeding rates.
Moosa (2017) reﬁned the ecosystem modeling work by
Mori and Butterworth (2006), mainly by incorporating/updating catch history and abundance estimates of species considered in the model, and by making some technical improvement to the model. Preliminary results supported the main ﬁndings of Mori and Butterworth (2006).
Moosa (2017) identiﬁed the following future work required: i) inclusion of a space limitation term for the fur
seals in the model so as not to increase their population
to unrealistically high values; ii) consideration of a set of
diﬀerently deﬁned regions as this might possibly better
represent the Antarctic minke whale abundance trends
and population trajectories; iii) further consideration of
the variation of the eﬀective annual natural mortality rateof krill, because this is likely causing the oscillations in the
krill dynamics; iv) further consideration of the variation of
the bounds imposed on carrying capacity of krill; and v)
further consideration of the variation in the proportion of
ﬁn whales assumed to feed south of 60°S.
This work is ongoing.

ECOSYSTEM MODELING IN THE WESTERN NORTHPACIFIC
Background
There were two main motivations for building ecosystemmodels in the western North Paciﬁc. The ﬁrst one was
the rapid decrease of Japanʼs ﬁsheries catch from 12,785
thousand tons in 1988 to 4,690 thousand tons in 2015
(GOJ, 2000; 2017). Ecosystem models including ﬁsheryresources can provide some insight to the reasons forchanges in stock status considering interaction among
species.
The second one was the possibility of competition
between marine mammals and ﬁsheries on prey speciesof economic importance. For example, common minke
whales (Balaenoptera acutorostrata) sightings occurred
close to the ﬁshing grounds of Paciﬁc saury (Cololabis
saira) oﬀ Kushiro. In fact, they fed mainly on Paciﬁc saury
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near the ﬁshing ground of the Paciﬁc saury. This observation suggested a relationship between common minke
whales and Paciﬁc saury from summer to autumn in thewestern North Paciﬁc (Tamura and Fujise, 2002). Ecosystem models can be used to investigate whether competition exists, for example by comparing ecosystem models
with and without the assumption of competition.
EwE model in the western North Paciﬁc
Objectives
Mori et al. (2009) applied the EwE model to investigate
the possible impact of whaling on the catch of otherspecies in the western North Paciﬁc ecosystem. They
also compared single-species MSY and multi-species
MSY. If there were substantial diﬀerences between the
two MSYs, the stock status derived from the two models
could be quite diﬀerent. In such a situation, multi-species
model should be used rather than single-species model
for assessment of the stocks.
Ecosystem model used
Ecopath is a static, mass-balanced snapshot of the system and Ecosim is a time dynamic simulation module forpolicy exploration (Christensen et al., 2005). The model
consist of the species from detritus to top predators. Ecopath is used to model tropic ﬂow within a food chain ofan ecosystem. Ecosim can be used to simulate temporalchange from the steady state described by the Ecopath
model under some harvesting scenarios.
The scenarios examined in Mori et al. (2009) were: i)harvesting 4% of the common minke whale population;ii) harvesting 4% of the sei whale (B. borealis) population;iii) harvesting 4% of Brydeʼs whale (B. edeni) population;iv) harvesting 4% of the common minke, sei and Brydeʼswhale populations; v) harvesting 4% of the sperm whale(Physeter macrocephalus) population; and vi) harvesting
4% of the four whale species.
For an initial test run, the impact of no harvesting andharvesting 4% of the whales for the coming 50 years oncatch of the ﬁshes, was made. Uncertainty of the data
input to Ecopath, functional response and trophic ﬂow(top-down or bottom-up) were considered. Type I functional response, which assumes that prey consumption
is in proportion to prey abundance up to the maximum(Holling, 1959) was assumed as the reference case, andthe Types II and III functional responses were used forsensitivity analyses.
Research area and data used
Mori et al. (2009) developed the EwE in the westernAn overview of the Institute of Cetacean Research work on ecosystem modeling in the Antarctic and western North Pacific
Table 1
List of the species/groups used in the EwE model and Trophic Level (TL) estimated by Ecopath (Mori et al., 2009).
Species/Group

TL

Species/Group

TL

Species/GroupTL

Minke whale
Brydeʼs whale
Sei whale
Other baleen whales
Sperm whale
Bairdʼs beaked whale
Short-ﬁnned pilot whale
Ziphiidae
Other toothed whales
Northern fur seal
Marine birds

3.99
3.83
3.73
3.23
4.17
4.15
4.40
4.24
4.46
4.08
4.24

Albacore
Sword ﬁsh
Skipjack tuna
Blue shark
Salmon shark
Lanternﬁsh
Neon ﬂying squid
Large surface squid
Small surface squid
Mid-deep water sea squid
Mackerels

4.08
4.81
3.97
4.27
4.35
3.06
4.12
3.41
3.01
3.11
3.30

Paciﬁc pomfret
SardineAnchovy (<8 cm)
Anchovy (≧8 cm)
Paciﬁc saury
Phytoplankton
Euphausiids
Copepods eaten by whales
Other Copepods
Detritus

4.20
2.30
3.04
3.04
3.12
1.00
2.18
2.00
2.00
1.00

North Paciﬁc (north of 35°N and west of 170°E), where
JARPNII surveys were conducted. The model consistsof 32 species/groups ranging from detritus to whales(Table 1). Estimated trophic level (TL) for each species isshown in Table 1. Higher TL represents the species playing
a role closer to top predator in the ecosystem. Input datarequired for the model were biomass, production, prey
consumption, diet composition and total ﬁshery catch ofeach predator in the target area. When production is notavailable, total mortality was used in the Ecopath insteadof production per biomass (Christensen et al., 2005).
Biomass, prey consumption and diet compositions
for common minke, Brydeʼs, sei and sperm whales werebased on JARPNII surveys. Details of the input data were
provided in Mori et al. (2009). Once the mass-balance
model was constructed, possible eﬀect on ﬁsheries ofthe species in the model were simulated by using Ecosim
under the harvesting scenarios examined.
Results
Results of Mori et al. (2009) suggested the following:i) 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 ofsome Japanese ﬁsheries resources (e.g. anchovy(Engraulis japonicus), Paciﬁc saury, skipjack tuna
(Katsuwonus pelamis), mackerels (Scomber japonicus, S. australasicus) will increase or not;ii) when sei and Brydeʼs 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;iii) when minke, sei and Brydeʼs whales are all harvested by 4% of their biomass, an increase in catchis expected for most of the ﬁsh resources (i.e. anchovy, skipjack tuna and mackerels), indicating theeﬀectiveness of harvesting several whale species
simultaneously; andiv) when 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, but in contrast, catch ofneon-ﬂying squid may increase.
The work by Mori et al. (2009) is being updated in thestudies by Murase et al. (2016) and Watari et al. (2018).
Murase et al. (2016) have focused on the interactions
between forage ﬁsh and their predators including target species of JARPNII (common minke, sei, Brydeʼs andsperm whales). They have updated the input data to themodels by considering the data available in the period
1994–2013 and made several technical improvements
following recommendations from the International Whaling Commission Scientiﬁc Committee (IWC SC). Watari
et al. (2018) have improved Ecopath model focusing on
the small pelagic ﬁshes to investigate their role in theecosystem by using two ecological indices.
Murase et al. (2016) and Watari et al. (2018) have identiﬁed several future data and work required to progress
the development of ecosystem modeling in the western
North Paciﬁc. Some suggestions to improve input of EwE
were the following: (i) consistency of spatial resolutionof input data is required, (ii) regional models are to be
developed within the EwE area, (iii) collection of dietcomposition data at regular interval is required becausesome of the diet composition data were old or obtained
outside the research area, (iv) resolution (the numbers
17Technical Reports of the Institute of Cetacean Research (2018)of species/group) and quality of data on non-commercial
and lower trophic level species should be improved and(v) evaluation of the sensitivity of Ecopath models to
input data is required.
This work is ongoing.
MRM in coastal areas
Objectives
Okamura et al. (2009) examined the eﬀects of predationof common minke whales on sandlance (Ammodytesspp.) stock oﬀ Sanriku region to investigate the eﬀect of
predation by the common minke whales in terms of MSY
of the sandlance population.
Model used
A Bayesian delay-diﬀerence approach (Hilborn and Walters, 1992; Meyer and Miller, 1999) was used to develop
a MRM-type ecosystem model. They focused on interaction between sandlance and common minke whales (Figure 2), and constructed a population dynamics model ofsandlance taking into account predation by the common
minke whales. In the analysis, functional response Types
I, II and III (Holling, 1959) were considered.
Research area and data used
The research area was the coastal area oﬀ Sanriku. Data
used in this modeling were CPUE series of juvenile andadult sandlance ﬁsheries, abundance estimate for adultsandlance, age, length and weight time series of sandlance during 1999–2005, catch series of sandlance bylift net with light ﬁshery, dip nets, and trawl-net ﬁsheryfrom 1901 to 2006 and stomach contents of the common
minke whales oﬀ Sanriku in 2003 obtained from JARPNII
surveys (Tamura et al., 2004).

Results
Under the scenarios of the linear functional response
curve (Type I), the resultant impact was so great that themedian value of MSY was increased by 154%. Whereasusing the constant functional response scenario (mostextreme functional form), the impact was much smaller,
where there was only a 17% increase of the medianvalue of MSY. This suggested that estimating functional
response is important to assess the impact of predationby the common minke whales.
Kitakado et al. (2016) updated the model work by
Okamura et al. (2009) by using new data as follows: i)catch and CPUE series of juvenile sandlances by lift nets
with light ﬁshery (1994–2015); ii) catch and CPUE seriesof adult sandlances by dip nets ﬁshery (1994–2015); iii)
time series of age composition data of sandlances takenfrom summer sampling survey (2002–2014); iv) time series of density estimate of sandlances (in number) during
summer sampling survey (2002–2014); v) consumptionof sandlances by common minke whales in 2005, 2006
and 2012 (JARPNII surveys); and vi) time series of abundance estimates in Sanriku region for the common minke
whales in 2004, 2005, 2006 and 2012.
Further analyses should be conducted to examine sensitivities of the results to the assumption of prior distributions. Because only the Type II functional response wasassumed in Kitakado et al. (2016), other forms of functional response and multiple-prey functional responsewith krill and anchovy are worthy to investigate. The
weight allocated among the components of the likelihood(likelihood relevant to abundance of juvenile sandlance,
likelihood relevant to abundance of adult sandlance,and likelihood relevant to age composition of sandlance)
should be carefully considered in future analyses (Kitakado et al., 2016).
This work is ongoing.

CONCLUDING REMARKS

Figure 2.

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Ecosystem model oﬀ Sanriku modeled in Okamura
et al. (2009) and Kitakado et al. (2016).

Progress has been made in the development of ecosystem modeling for some particular objectives related toﬁsheries management and ecosystem structure and dynamics, in both the Antarctic and western North Paciﬁc.
Biological and abundance data of the species consideredin the models are important. In the case of whale species
such data came from ICR surveys. Several research needs
were identiﬁed which should be addressed to progress
the work on ecosystem modeling work and potentiallycontribute to resources management taking into accountmulti-species considerations.

An overview of the Institute of Cetacean Research work on ecosystem modeling in the Antarctic and western North Pacific

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