Journal of International Fisheries
vol.13，2014▶Article ◀

Methodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tuna
Minoru TADA† and Jun SAKAGUCHI（Kinki University）
E-mail：†tadam@nara.kindai.ac.jp【abstract】
Regulatory measures for Pacific bluefin tuna fisheries have just been introduced by theWestern and Central Pacific Fisheries Commission (WCPFC). Whether stricter
regulations will be adopted is crucial in determining tuna prices and the profitability oftuna farming worldwide. As reliable resource data for this species are not available, wedeveloped a new methodology to estimate fishery resource movement using catch data.
The method is a combination of the surplus production model, which has anenvironmental factor term in addition to the original specification, and the supply
function, which explains catch amount with respect to resources and other variables
such as price and time trend. This methodology can be applied to open access fisheries,in which the bluefin tuna fishery in the Pacific Ocean has been the typical one. The
results indicate that both environmental factors, represented by the Pacific Decadal
Oscillation (PDO), and fishing activity are crucial in determining changes in resources.
However, appropriate combinations of PDO coefficient (𝜃), intrinsic growth rate (𝑟) andcarrying capacity (𝐾) that satisfy the parametric conditions of the supply function were
widely distributed; thus it was difficult to specify them by statistical criteria. Therefore,
the estimated bluefin tuna resource trends based on such parameters were also widelydistributed: as some of them were over maximum sustainable yield (MSY) and others
were are below it for the latest decade.
【keywords】
Pacific bluefin tuna, surplus production model, supply function

1．Introduction
Japan is the market that consumes the most bluefin tunas in the world. However,resources of Atlantic and Southern bluefin tuna have become so depleted (1) that theInternational Commission for the Conservation of Atlantic Tunas (ICCAT) and the- 13 -

Minoru TADA and Jun SAKAGUCHI

Commission for the Conservation of Southern Bluefin Tuna (CCSBT) have strictly
regulated the catches.
Consequently, the trend of Pacific bluefin tuna catching is crucial for determining
the price of bluefin tuna in the Japanese markets, which will be transmitted to foreign
markets and affect the profitability of tuna farming in the Mediterranean Sea,
Australia and Mexico.
Regulatory measures for the bluefin tuna fisheries in the Pacific Ocean have justbeen introduced by the Western and Central Pacific Fisheries Commission (WCPFC).
As the resource analyses for this species have not been robust as mentioned in the next
section, it is unclear whether stricter measures will be introduced.
Therefore, we analyzed factors that cause changes in the resource levels of Pacificbluefin tuna by developing a new methodology to estimate resource trends, which is acombination of the surplus production model and the supply function.

(yen/kg)(tons)
80,0006,000

70,0005,000

60,0004,000

50,00040,0003,000

30,0002,000

20,0001,000

10,0000Local farmings
Import of SBT
Import of NBT
Local landings of SBT
Local landings of NBTNBT Price (Fresh, Average)
NBT Price (Fresh, Tsukiji)

0
1975

1980

1985

1990

1995

2000

2005

2010

Figure 1 Supply of bluefin tunas to the Japanese market and the priceSources:
1) Local landings, “Production Statistics of Fisheries and Aquaculture”, MAFF.
2) Imports, “Trade Statistics”, MOF.
3) Local farmings, Various sources including those based on the interviews tobusiness persons related to bluefin tuna.
4) Prices: Nominal prices of the year average, “Statistics of Fisheries Distribution”(1965-2006), Fishery Agency and “Statistics of Tokyo Central Wholesale
Markets” (2004-2011), Tokyo.

- 14 -

Methodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tuna

2．The Japanese bluefin tuna market
Bluefin tuna is mostly used for sashimi and sushi in Japan. As Miyake et al. (2010)
described, there are two types of consumers in the Japanese market: those who prefer
red meat and those who prefer fat content, and the latter has increased due to the
influence of a westernized diet. Belt conveyer sushi restaurants have been popularsince the 1990s, and the demand for farmed bluefin tuna with high fat content has
grown. The recent supply of bluefin tuna into the Japanese markets is composed ofdomestic landed wild tuna, domestic farmed tuna, and imported farmed tuna.
Before 1980, almost all bluefin tunas were domestically landed as shown in Figure 1,
where the northern bluefin tuna (NBT) is an aggregation of the Atlantic bluefin tuna
(ABT) and the Pacific bluefin tuna (PBT). However, domestic landings began todecrease in the late 1980s, whereas the demand for tunas increased due to theeconomic bubble. The sharp decrease in domestic landings was a result of resource
reduction caused by over-fishing and/or climate change. Subsequently, the wholesale
price of northern bluefin tuna sharply rose from 3,000 yen/kg to more than 4,000
yen/kg. This accelerated the imports of bluefin and bigeye tunas. Bigeye tuna is a
substitutional good for wild bluefin tuna and it has a standard class position in thesashimi market.
The imports of southern bluefin tuna (SBT) began in 1991, after the successful
application of fattening technology. At the same time, the imports of ABT, which
comprise most of the NBT imports, also increased, and the proportion of farmed tuna
among imported bluefin tuna has been increasing since then (Ono (2012), pp.124-126).
However, both SBT and ABT face serious resource constraints and catch controls, andtheir catch amount has recently become stagnant.
The CCSBT was established in 1994 to conserve SBT resources, with Japan,
Australia and New Zealand as the initial members. Before that, these countries
introduced a catch quota system in 1985, and the quota of each country has sincedecreased. For example, the countries’ total catch quota was 38,650 tons in 1985;
however, this decreased to 11,750 tons in 1989 and has remained at that level until
1997.
Even after the issues of non-member involvement (Pintassilgo and Duarte (2001))
and the Japanese experimental fishery plan (Polacheck (2002) and Komatsu and Endo
(2002)) ended, the CCSBT continued to reduce the quota, but the tuna resources neverrecovered. Historically, the lowest quota was 9,449 tons for the year 2011, which was- 15 -

Minoru TADA and Jun SAKAGUCHI

one-fourth of the quota for 1985. Of this quota, 2,261 and 4,015 tons were allocated toJapan and Australia, respectively. The 2012 quota was slightly relaxed to 10,449 tons.
Regarding the east ABT, which comprises most of the ABT including the west ABT,
the catch ratio has rapidly increased to more than 25%, and the estimated spawning
stock biomass in 2005 was nearly 100,000 tons which is one-third of the estimation in1975 (ICCAT (2009)). Therefore, the catch quota that exceeded 30,000 tons when it wasestablished in 1998 was reduced to 13,500 tons in 2010, and then reduced to 12,900
tons again for the years 2011-12 after the annual meeting was held in November 2010.
The quota was slightly relaxed to 13,400 tons for the year 2013.
In Japan, the production of farmed tuna has been steadily increasing, and reachedapproximately 10,000 tons in 2010, which exceeds the each production of farmed SBT
and ABT, and this production growth has compensated for the reduction of imports. In
addition, full-cycle tuna farming technology is spreading in Japan, and accounts fornearly 10% of local farm production (Kumai (2012), pp.11), and the ratio is currently
rising.
However, the production of farmed tuna is not expected to grow as rapidly as it did
previously because of the limitation of appropriate farming sites in Japan. In addition,rising feed cost is another concern, because the Japanese farming technology adopts along-term fattening process, whether it utilizes full-cycle technology or not, and it
takes 2-3 years to raise wild or artificial seedlings to the 50-60 kg size suitable for sale.
Therefore, the trend of the wild PBT catch is a key factor that will determine thesupply and price of bluefin tuna in the future. Most of the Japanese locally landed PBTfrom the West Central Pacific Ocean is caught by purse seine, longline, troll, or set net.
Other major countries catching the tuna include Taiwan, USA and Mexico.
Resource evaluations for bluefin tuna in the Pacific Ocean are not yet robust. The
International Science Committee (ISC) presented very different resource estimatesderived by applying different methodologies such as virtual population analysis andstock synthesis (ISC (2006), ISC (2008)). In addition, the ISC (2012) presented a sharp
decreasing trend in the spawning stock biomass after the 2000 year in particular.
Therefore, regulatory measures have just been introduced by the WCPFC. The
annual meeting held in 2010 resulted in regulating fishing efforts for 2011-12 below
the 2002-04 level, and in keeping the annual juvenile catch amount below the 2002-04
level. Before this decision by the WCPFC, the Ministry of Agriculture, Forestry and
Fisheries (MAFF) of Japan decided on an action plan toward the effective conservationand management of resources (MAFF (2010) (2)). The plan includes a closure period, a- 16 -

Methodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tunacatch size limit, an individual quota system for large and medium scale purse seine
offshore fisheries, and the registration of vessels with a mandatory catch reporting
requirements for small scale coastal fisheries. In addition, the plan was encouraged toinclude a catch limit for fish under and over 30kg weight respectively for offshore
fisheries as announced on March 25, 2011.

3．Methodology
Fishing effort is a concept that refers to the “aggregated inputs for fishing” andincludes vessels, fishing gears, number of labor days, etc. Catch per unit effort (CPUE)
data are generally used for resource estimates, and those of longline fisheries arewidely used to estimate tuna resources. However, CPUE data are generally collected inhigh-density resource areas where fisheries operate rather than in randomly sampledareas (Ishii (1996), pp.176). This was the central issue in the Australia-Japan disputeconcerning the Japanese experimental fishery plans for SBT. Furthermore,
standardizing of CPUE between different fishing gears such as longline and purse
seine (Fisheries Research Agency (2010) (3)), and the CPUE before/after introducing
catching innovations such as fish-find sonar, aerial observation and fish aggregating
devices (FADs) are the additional constraints of using CPUE data.
Therefore, we estimate resources by applying the surplus production model
developed by Schaefer (1954) and Clark (1985). With the model, resources can be
estimated using catch data. There is an alternative method that applies a long-term
supply curve that has a backward-bending portion for octopus resources in Morocco(Yagi et al. (2009)). This method can be applied to the fishery because the price isexogenous for Morocco as it is a small producer. However, the price is endogenous for
PBT producers, and the price-quantity relation draws a demand curve rather than asupply curve(4).
Based on the surplus production model, resources at the end of each period (𝑆𝑡 ) arerepresented as follows:
𝑆𝑡 = 𝑆𝑡−1 + 𝑟𝑆𝑡−1 (1 − 𝑆𝑡−1 ⁄𝐾) − 𝑄𝑡

(Eq.1)

where 𝐾 is the maximum carrying capacity of the species, 𝑟 is the intrinsic growthrate, 𝑄 is the catch amount, and the subscript t is the year. On the right side of (Eq.1),
if the catch is equal to the second term which is growth of the resource (𝑑𝑆⁄𝑑𝑡), thenthe resource is stable as 𝑆𝑡 = 𝑆𝑡−1, and the catch is termed a sustainable yield (SY). In
addition, if S = 𝐾⁄2, then 𝑑𝑡𝑆⁄𝑑𝑡 is maximized, and the catch is termed a maximum- 17 -

Minoru TADA and Jun SAKAGUCHI

sustainable yield (MSY). If the resource data are available, the parameters 𝐾 and 𝑟
can be estimated by applying the least squares regression method. For example,
Grafton et al. (2000) estimated that the intrinsic growth rate was 0.3 by providing an
exogenous 𝐾 for Canadian cod.
For the case of PBT, however, environmental conditions as well as catch amounts
are crucial for determining the resource fluctuations. This phenomenon is known as a“regime shift” and is observed for ABT as well (Ravier and Fromentin (2001)).
Kawasaki (2005), pp.133-137 presented a drastic change in catch amount per fishing
day for the Japanese coastal longline bluefin tuna fisheries before and after 1990.
Miller (2007) presented a negative correlation between the El Niño 3 Index and thecatch amount of skipjack by purse seine fisheries in the West Central Pacific Ocean, in
which the author mentioned that the Pacific Decadal Oscillation (PDO) had recently
been recognized as having significant impacts on marine fisheries.
PDO represents the deviation in sea surface temperatures against the warming
trend, and a positive PDO indicates low sea surface temperature in the North Central

Pacific Ocean with the exception of the East Pacific Ocean beside the North
American Continent. There are some alternatives to PDO such as the Southern
Oscillation Index (SOI), a representative indicator of the El Niño phenomenon, and
the Aleutian Low Pressure Index (ALPI), which correlate each other. We selected PDO
based on the statistical criteria used for estimating supply functions as shown in(Eq.3).
Regarding the further precise PDO-resource relation for tuna species, Lehodey et al.
(2003) showed opposite recruitment patterns of tropical species such as skipjack andyellowfin and of subtropical species such as albacore against PDO index and SOI. A
similar relation was found between SOI and PBT recruitment (Inagake and Uehara(2003)). In addition, Lima and Naya (2011) presented a close relation between skipjack
CPUE data in the Western Pacific Ocean and SOI and PDO.
Then we modified (Eq.1) to
𝑆𝑡 = 𝑆𝑡−1 − 𝜃 ∗ 𝐷 ∗ 𝑃𝐷𝑂5𝑡 ∗ 𝑟𝑆𝑡−1 (1 − 𝑆𝑡−1 ⁄𝐾) − 𝑄𝑡

(Eq.2)

where the second term on the right side represents the influence of environmental
change in which 𝑃𝐷𝑂5 is the 5-years moving average of the PDO index. 𝜃 is thecoefficient representing the degree of influence to the resources when environmental
conditions are not favorable, and 𝐷 is the dummy variable that is 1 when 𝑃𝐷𝑂5 > 0
and is 0 when 𝑃𝐷𝑂5 ≤ 0 , indicating that existing resources decrease when theenvironmental conditions are unfavorable for the species (5).
- 18 -

Methodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tuna

Because reliable resource data are unavailable, we generated data by giving various
values to 𝜃, 𝑟 and 𝐾 in (Eq.2), based on the assumption that 𝑆 = 𝐾 in 1945 becauseof a long fishing moratorium during the World War II(6). Then, we selected the
combinations of the three parameters that provide an appropriate supply function,
which are specified as follows:
𝑙𝑛 𝑄𝑡 = 𝑎 𝑙𝑛 𝑆𝑡 + 𝑏 𝑙𝑛 𝑃𝑡 + 𝑐 𝑙𝑛 𝑃𝑂𝐼𝐿𝑡 + 𝑑 𝑇 + 𝑒(Eq.3)

where 𝑃 and 𝑃𝑂𝐼𝐿 represent a series of past prices of bluefin tuna and crude oil,respectively, and 𝑇 is the time trend representing technological progress to catch
bluefin tuna. In the equation, 𝑎, 𝑏, 𝑐 and 𝑑 are coefficients to be estimated, and 𝑒 is aconstant term. The coefficients have constraints such as 0 < 𝑎 < 1, 𝑏 > 0, 𝑐 < 0 and𝑑 > 0. The sources of the variables are listed in Table 1.
The original specification of (Eq.3) was used by Eide et al. (2003) as the
Cobb-Douglas function as 𝑄 = 𝐶𝑜𝑛𝑠𝑡.∙ 𝐸𝑓𝑓𝑜𝑟𝑡 𝛼 ∙ 𝑆𝛽 , which is a further flexible
modification of 𝑄 = 𝐶𝑜𝑛𝑠𝑡.∙ 𝐸𝑓𝑓𝑜𝑟𝑡 ∙ 𝑆 developed by Schaefer (1954). (Eq.3) modifies
such relations by converting the quantity of inputs into price terms as used in micro
economic analysis.

4．Analysis and results
4-1．Preliminary analysis
Bluefin tuna prices affected the catch amount through changes in catch efforts, andtheir relation is presented in Figure 2. The catch amount sharply grew during 1950-55,
which was an adjustment process for fishing efforts from the period the World War II
destruction to the restart of normal fishing. The catch amount was very high during
1955-65 because of the high amount of resources. A negative correlation was observed
between the two variables after 1965.
Regarding the influence from the price to catch amount, about a five year’ time lag
was observed, implying that it takes around five years for fishermen to recognizechanges in long-term price level and adjust their effort levels to the new economic
profitability of bluefin tuna fisheries. However, the drastic decline in catch during theearly 1980s following the price decline in the late 1970s, and the prolonged low catchlevel that continued into the early 1990s may have been too extreme compared withthe decline in catch amount around 1970 against the very low price level around 1965.
Next, we compared the catch amount with environmental conditions represented by
PDO as shown in Figure 3. The drastic decline in the catch amount in the 1980s was- 19 -

Minoru TADA and Jun SAKAGUCHI(yen/kg)(tons)
45,0006,000

40,0005,000
35,0004,000

30,00025,0003,000
20,0002,000

15,00010,000

1,000
5,000
0

0
1950

1960

1970

1980

Price in Japan

1990

2000

2010

Catch amountFigure 2 Price of Pacific bluefin tuna in Japan and the catch amount
Sources:
1) Catch amount, “Fish Stat”, FAO.
2) Price, Same as Figure 1. Price is deflated by consumer price index(2010 year base). The Tokyo price is discounted by 13% to adjust
the price difference between average in Japan (1965-2006) andthat in Tokyo (2004-2012), and is connected to the averagewholesale prices(7).

negatively correlated with the PDO index, whereas the correlation was unclear inother periods.
4-2．Estimate of supply function(Eq.3) was estimated by the ordinary least squares regression for 1971-2011. FAO
Fishstat data, which is available for long-term periods after 1950 and is almost
identical to the WCPFC data, was used for catch amount (𝑄).
We employed the average of the 4-6 year lagged price of bluefin tuna; i.e.
((𝑃𝑡−4 + 𝑃𝑡−5 + 𝑃𝑡−6 )⁄3) deflated by the Japanese consumer price index for the price (𝑃).
This was based on the preliminary analysis shown above and a trial and error process
to estimate (Eq.3), implying that it takes 4-6 years to adjust fishing efforts such as
vessels and labor to price changes. In addition, we could not obtain a negative sign for- 20 -

Methodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tuna(tons)
1.5

45,00040,0001
35,0000.5

30,00025,00001945 1950

1960

1970

1980

1990

2000

-0.5

2010

20,00015,00010,000

-1
5,000
-1.5

0
PDO Index (5 years moving average)

Catch amountFigure 3 Pacific Decadal Oscillation index and the catch amount of Pacific bluefin tuna
Sources:1) PDO Index: Japan Meteorological Agency.
2) Catch amount: “Fish Stat”, FAO.

the coefficient 𝑐. This is because changes in the crude oil price can be applied across all
types of fisheries, not only those dedicated to PBT. Therefore, the oil price variable(𝑃𝑂𝐼𝐿) was eliminated from (Eq.3) for the estimate.
Regarding the resource variable (𝑆), we prepared various values for parameters 𝜃, 𝑟
and 𝐾 in (Eq.2), and calculated 𝑆 that should have taken place for the period of
1950-2011, under each combination of the three parameters. Here, the catch data for
1945-49 were considered as zero because the catch amount in 1950 was very small (6).
After that, appropriate combinations of the three parameters that satisfied theconditions 0 < 𝑎 < 1, 𝑏 > 0 and 𝑑 > 0 in (Eq.3) were selected.
4-3．Results
The summary of the estimated results is presented in Figure 4, and the estimatedparameters for each equation are listed in Table 1. In the figure and the table, theupper limit of 𝐾 is set to 2 million tons, as the estimated supply functions with 𝐾 > 2
million tons presented almost identical determinant coefficients and parameters. Thisis because the catch amount was very small compared with the resources, and the- 21 -

Minoru TADA and Jun SAKAGUCHI

Figure 4 Appropriate combinations of parameters in surplus production model (Eq.2)
Notes:
1) Value in each cell is a determinant coefficient of estimatedsupply function (Eq.3).
2) 𝜃: influence of PDO to the stock, 𝑟: intrinsic growth rate,

𝐾: maximum carrying capacity
influence of the catch to resources is very limited when 𝐾 > 2 million tons.
As shown in Figure 4, the possibility that 𝐾 ≤ 0.5 million tons was very small. The
ranges of 𝜃 and 𝑟 were 0.24 < 𝜃 < 0.56 and 0.16 < 𝑟 < 0.40. respectively. The left
upper area of the 𝜃-𝑟 matrix was probable due to the high density of cells that cansatisfy the (Eq.3) parameter conditions.
In addition, the cells with 𝜃 = 0 or 𝑟 = 0 could not provide a satisfactory supply
function. The estimates of 𝜃 < 0.16 or 𝑟 < 0.08 provided unsatisfactory signs for theelasticity of resources; i.e. 𝑎 > 1 or 𝑎 < 0 in (Eq.3), Otherwise the estimatedresources presented were fully depleted. These estimates are presented in the
Appendix. These results indicate that both environmental factors and fishing activities
are crucial in determining resource changes.
The estimated parameters, 𝑎, 𝑏 and 𝑑 in (Eq.3) were widely distributed as shownin Table 1. The production elasticity of resources (𝑎) ranged from 0.31 to 0.95, and thiswas consistent with a harvest function study of the Norwegian cod fisheries that
estimated an elasticity of 0.424 (Eide et al. (2003)). The price elasticity of supply (𝑏)- 22 -

Methodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tuna

Table 1 Estimated parameters of supply function (Eq.3)
𝑙𝑛 𝑄𝑡 = 𝑎 𝑙𝑛 𝑆𝑡 + 𝑏 𝑙𝑛 𝑃𝑡 + 𝑐 𝑙𝑛 𝑃𝑂𝐼𝐿𝑡 + 𝑑 𝑇 + 𝑒
θ
0.24
0.32
0.40
0.24
0.24
0.32
0.40
0.48
0.56
0.24
0.24
0.32
0.40
0.48
0.24
0.24
0.32
0.40
0.48
0.48
0.56

K
0.5
0.5
0.5
1.0
1.0
1.0
1.0
1.0
1.0
1.5
1.5
1.5
1.5
1.5
2.0
2.0
2.0
2.0
2.0
2.0
2.0

ｒ
0.32
0.32
0.40
0.16
0.24
0.24
0.32
0.32
0.40
0.16
0.24
0.24
0.24
0.32
0.16
0.24
0.24
0.24
0.24
0.32
0.32

a (t-value)
0.838 (5.0)
0.617 (4.9)
0.490 (5.1)
0.951 (5.3)
0.821 (5.4)
0.573 (5.2)
0.489 (5.5)
0.404 (5.0)
0.328 (5.1)
0.757 (5.3)
0.848 (5.6)
0.594 (5.5)
0.442 (5.2)
0.391 (5.5)
0.756 (5.4)
0.861 (5.6)
0.606 (5.6)
0.449 (5.4)
0.397 (5.1)
0.405 (5.6)
0.310 (5.2)

b (t-value)
0.291 (1.4)
0.294 (1.4)
0.418 (1.9)
0.130 (0.7)
0.380 (1.8)
0.407 (1.9)
0.447 (2.1)
0.346 (1.6)
0.497 (2.2)
0.379 (1.8)
0.374 (1.8)
0.437 (2.1)
0.426 (1.9)
0.487 (2.2)
0.412 (1.9)
0.369 (1.8)
0.436 (2.1)
0.462 (2.1)
0.331 (1.6)
0.479 (2.2)
0.485 (2.2)(Estimated period: 1971-2011)
d (t-value)
0.002 (0.5)
0.014 (2.9)
0.001 (0.4)
0.043 (4.8)
0.003 (0.8)
0.009 (2.1)
0.001 (0.3)
0.018 (3.4)
0.002 (0.5)
0.014 (3.0)
0.001 (0.4)
0.004 (0.9)
0.010 (2.4)
0.002 (0.4)
0.010 (2.4)
0.001 (0.2)
0.002 (0.6)
0.006 (1.5)
0.024 (4.0)
0.000 (0.0)
0.005 (1.4)

2

R
0.41
0.40
0.42
0.44
0.45
0.43
0.45
0.41
0.42
0.44
0.46
0.46
0.43
0.45
0.45
0.47
0.47
0.45
0.42
0.47
0.43

DW
1.5
1.5
1.5
1.6
1.6
1.5
1.6
1.5
1.5
1.5
1.6
1.6
1.5
1.6
1.5
1.6
1.6
1.6
1.5
1.6
1.5

Note: Coefficient c was eliminated from the equation due to the positive
sign of the estimated value.
Sources:1) Q: “Fish Stat,” FAO (ref. Figure 2 and Figure 3).
2) S: Generated by (Eq.2).
3) P: “Statistics of Fisheries Distribution (1965-2006),” Fishery Agency,
and “Statistics of Tokyo Central Wholesale Markets (2004-2011),”
Tokyo Metropolitan Central Wholesale Market (ref. Fig. 1 and Fig. 2).
4) POIL: Toyokeizai Data Bank, Original Source: “Trade Statistics,” MOF.
5) T: Time trend, 1950 year=1, 2011 year=62.

ranged from 0.13 to 0.50, and the technological progress rate (𝑑) ranged from 0.0% to4.3% per year.
Then, we compared the estimated parameters for PBT with those for ABT (Tada etal . (2011)) and SBT (Tada (2012)). The estimated 𝐾 values were not much different
across the species. The probable 𝐾 ranged 1.1-1.5 and 1.6-2.2 million tons for ABT and
SBT, respectively. However, the estimated 𝑟 value for PBT was 0.16-0.40. This value
was much higher than that for ABT and SBT, which were 0.03-0.06 and 0.015-0.04,
respectively. This difference in 𝑟 is considered to be caused by the functional
difference in the surplus production model. The model used in this study includes aregime shift factor, while others did not. Therefore, PBT resources in this model
sharply decreased when environmental conditions were unfavorable for the species.
- 23 -

1945 1950

- 24 -

θ =0.32, r=0.32

1980

1990

2000

2010

Figure 5 Estimated resource trends

0

200,000

400,000

600,000

800,000

1970

1980

1990

2000

2010

θ =0.24, r=0.24
θ =0.48, r=0.32

θ =0.24, r=0.16

θ =0.40, r=0.24

θ =0.32, r=0.24

19451950

θ =0.48, r=0.24

1970θ =0.48, r=0.32

θ =0.24, r=0.24

1960

1990

1990

θ =0.56, r=0.32

θ =0.32, r=0.24

19804) K=2 million tons

θ =0.48, r=0.32

1980
θ =0.24, r=0.24

1970θ =0.40, r=0.32

19602) K=1 million tons

θ =0.24, r=0.16

19451950

θ =0.24, r=0.16

0500,000

1,000,000

1,500,000

2,500,000(tons)

1,000,000

1960θ =0.40, r=0.40

2,000,000

19451950

1970

3) K=1.5 million tons

θ =0.24, r=0.32

1960

0

200,000

400,000

600,000

1,200,000

1,400,000(tons)
1,600,0000

100,000

200,000

300,000

800,000

400,000(tons)
1,200,000
1,000,000

1) K=0.5 million tons

500,000(tons)
600,0002010

2010
θ =0.40, r=0.24

2000

θ =0.56, r=0.40

θ =0.32, r=0.24

2000

Minoru TADA and Jun SAKAGUCHIMethodology to Estimate Fishery Resources using Catch Data and its
Application to Pacific Bluefin Tuna

The 𝑟 value in this study was higher than those of other species to recover fromdepletion under favorable environmental conditions. This result indicates that 𝑟
value depends on the model specification.
Finally, the resource trends were calculated based on the parameters 𝐾, 𝜃 and 𝑟,
and the result is presented in Figure 5. As known in the figure, some recent estimates
resource levels were over MSY, which is equal to 𝐾/2, and some were below MSY,
indicating that the ranges of the three parameters were not narrow enough to see clear
resource trends.

5. Conclusion
We developed a methodology to estimate fishery resource movement using catch
data. The model is a combination of the surplus production model, which has anenvironmental factor term in addition to the original specification, and the supply
function that explains catch amount with respect to resources and other variables suchas tuna price and time trend. This methodology is applicable to open access fisheries,in which the bluefin tuna fishery in the Pacific Ocean has been the typical one. It has
an advantage in being able to provide information needed to discuss resource trends
with catch data that is more accessible than CPUE data.
Our results indicate that both environmental factors, represented by PDO, andfishing activity are crucial in determining resource changes. However, the appropriate
combinations of the coefficient of PDO (𝜃), intrinsic growth rate (𝑟) and carrying
capacity (𝐾 ) were widely distributed, and so it was difficult to specify them bystatistical criteria. Therefore, the estimated trends in bluefin tuna resources based onsuch parameters were also widely distributed, as some were over MSY and others were
below it for the latest decade.
As this methodology can be applied to various types of open access fisheries forwhich reliable resource data and fishing efforts data are unavailable, a further refined
simultaneous estimation procedure using the surplus production model and supply
function are needed so that more parameters can be used in the surplus production
model (Eq.1) and more environmental variables representing regime shifts can be usedin the modified surplus production model (Eq.2).

Notes(1) The latest spawning stock biomass reports are presented in ICCAT (2012) and CCSBT- 25 -

Minoru TADA and Jun SAKAGUCHI(2011).
(2) MAFF (2010) “Announcement by the Ministry of Agriculture, Forestry and Fisheries
on Actions toward Effective Conservation and Management for Pacific Bluefin Tuna,”
May 11. http://www.jfa.maff.go.jp/j/kokusai/kanri_kyouka/index2.html(3) Fisheries Research Agency [FRA] (2010) “Fluctuations and Evaluations of Fisheries
Resources(gyogyoshigen

no

hendo

to

shigenhyouka

nitsuite),”Statusofinternational fisheries resources (heisei 22 nendo kokusai gyogyoushigen no genkyo)(in Japanese). http://kokushi.job.affrc.go.jp/H22/H22_02.pdf(4) Regarding the case of Atlantic bluefin tuna and Southern bluefin tuna, for which thecatch quota was applied and there are strong evidences of resource depletion, Tada etal . (2011) and Tada (2012) applied the surplus production model (Eq.1) to estimate
resource trends. The parameters 𝑟 and 𝐾 that satisfied 0 < 𝑆 < 𝐾 ⁄2

for the latest

period were selected for the estimates.
(5) Instead of the specification (Eq.2), we applied alternative specifications such as𝑟 = 𝑟0 + 𝜃 ∗ 𝑃𝐷𝑂5𝑡 or 𝐾 = 𝐾0 + 𝜃 ∗ 𝑃𝐷𝑂5𝑡 in (Eq.1), in which 𝑟0 or 𝐾0 is a constant.
However, the estimated resource trends based on the specifications could not trace thedrastic decline in the catch that occurred in the early 1980s.
(6) The Japanese catch of bluefin tuna was very limited during the World War II(Okamoto (2004), p.19 and p.24). In addition, the bluefin tuna catch in the early 1950s
by countries other than Japan was limited. We applied an alternative assumption of
𝑆 = 0.8𝐾 in 1945, and estimated the resource trends. Using this assumption, it took8-13 years for the estimated resource trends to catch up with the trend using theassumption of S = 𝐾 in 1945.
(7) As shown in Figure 1, the average wholesale price data terminated in 2006. Therefore,
we connected it to the Tokyo wholesale price data as indicated in Figure 2.

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- 28 -

Journal of International Fisheries
vol.13，2014▶Article ◀

Appendix
Estimated results of (Eq.3) under the condition of 𝜃 < 0.24 or 𝑟 < 0.16

Estimated 𝑎, 𝑏 and 𝑑 of (Eq.3) under the conditions of each cell (1)－(28) above.

𝑙𝑛 𝑄𝑡 = 𝑎 𝑙𝑛 𝑆𝑡 + 𝑏 𝑙𝑛 𝑃𝑡 + 𝑐 𝑙𝑛 𝑃𝐷𝑂𝐿𝑡 + 𝑑 𝑇 + 𝑒
(1)
(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)
(28)

(0 < 𝑎 < 1, 𝑏 > 0, 𝑐 < 0, 𝑑 > 0)

K

θ

ｒ

a (t-value)

2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5

0
0
0
0
0
0
0
0
0.16
0.16
0.16
0.16
0.24
0.24
0.24
0.24
0
0
0
0
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16

0
0
0
0
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.24
0.24
0.24
0.24

-8.490 (-2.8) 0.640 (1.8)-0.170 (-2.8)-1.826 (-2.5) 0.514 (1.5)-0.080 (-2.5)
Resource depletion
Resource depletion-9.946 (-1.8) 0.023 (0.1)
0.001 (0.2)-5.523 (-1.4) -0.025 (-0.1) -0.000 (-0.1)-1.561 (-0.8) -0.089 (-0.3) -0.003 (-0.5)
Resource depletion
1.274 (5.1)
0.213 (1.0)
0.041 (4.4)
1.473 (4.9)-0.036 (-0.2) 0.068 (4.6)
Resource depletion
Resource depletion
0.402 (2.2) -0.358 (-1.4)
0.041 (2.2)
Resource depletion
Resource depletion
Resource depletion-28.57 (-4.7) 0.504 (2.1)
0.005 (1.1)-20.16 (-4.6) 0.495 (2.0)
0.006 (1.2)-11.43 (-4.1) 0.451 (1.7)
0.008 (1.5)-0.286 (-0.4) -0.104 (-0.4) -0.000 (-0.0)
1.248 (5.5)
0.376 (1.8)
0.008 (1.7)
1.242 (5.4)
0.363 (1.7)
0.010 (2.0)
1.248 (5.2)
0.311 (1.5)
0.016 (2.9)
Resource depletion
1.373 (5.3)
0.273 (1.3)
0.001 (0.3)
1.358 (5.2)
0.273 (1.3)
0.001 (0.3)
1.324 (5.1)
0.273 (1.3)
0.002 (0.5)
1.262 (4.7)
0.182 (0.8)
0.012 (2.2)- 29 -b (t-value)d (t-value)

2

DW

0.19
0.16

1.0
1.0

0.09
0.07
0.03

0.9
0.8
0.9

0.43
0.41

1.5
1.5

0.12

1.0

0.40
0.38
0.33
0.01
0.47
0.46
0.44

1.1
1.0
1.0
0.9
1.6
1.6
1.6

0.45
0.44
0.43
0.39

1.6
1.5
1.5
1.5

R