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Transoceanic migration rates of youngNorth Pacific albacore, Thunnus alalunga,from conventional tagging data
Momoko Ichinokawa, Atilio L. Coan, Jr., and Yukio Takeuchi
Abstract: This study summarizes US and Japanese historical North Pacific albacore (Thunnus alalunga) tagging data anduses maximum likelihood methods to estimate seasonal migration rates of young North Pacific albacore. Previous studiesrelated to North Pacific albacore migration have found that the frequency of albacore migrations is difficult to quantify be-cause of inadequate amounts of tags released by the US tagging program in the western Pacific. Use of the combined Ja-pan and US tagging data solves this problem. This study also incorporates specific seasonal migration routes, hypothesizedin past qualitative analyses, to avoid overparameterization problems. The estimated migration patterns qualitatively corre-spond to those from previous studies and suggest the possibility of frequent westward movements and infrequent eastwardmovements in the North Pacific. This frequent westward movement of young albacore in the North Pacific would corre-spond to a part of albacore life history in which immature fish recruit into fisheries in the western and eastern Pacific andthen gradually move near to their spawning grounds in the central and western Pacific before maturing.
Resume : Notre etude fait la synthese de donnees archivees americaines et japonaises de marquage du germon (Thunnusalalunga) du Pacifique Nord et utilise des methodes de vraisemblance maximale pour estimer les taux saisonniers de mi-gration des jeunes germons du Pacifique Nord. Des etudes anterieures sur la migration du germon dans le Pacifique Nordont trouve qu’il est difficile de quantifier la frequence de migration du germon a cause du nombre insuffisant d’etiquettesliberees par le programme americain de marquage dans l’ouest du Pacifique. L’utilisation combinee des donnees de marqu-age japonaises et americaines resout ce probleme. Notre etude incorpore aussi des routes specifiques de migration saison-niere, fixees par hypothese dans les etudes qualitatives anterieures, afin d’eviter les problemes de surparametrage. Lespatrons de migration estimes correspondent qualitativement a ceux des etudes anterieures et laissent croire a la possibilitede nombreux deplacements vers l’ouest et des deplacements plus rares vers l’est dans le Pacifique Nord. Ces deplacementsfrequents vers l’ouest des jeunes germons dans le Pacifique Nord devraient correspondre a la periode du cycle biologiquependant laquelle les poissons immatures sont incorpores dans les peches dans l’ouest et l’est du Pacifique; ils se deplacentensuite graduellement vers leurs lieux de fraye dans le centre et l’ouest du Pacifique avant d’atteindre la maturite.
[Traduit par la Redaction]
Introduction
The dispersion of individuals and populations in nature isusually a very difficult part of life history to quantify(Begon et al. 1996). This is especially true for transoceanicmigrating pelagic fish. The causes of these transoceanicmovements are ecologically important in explaining the spa-tial distribution of these organisms. In addition, the migra-tion patterns of various pelagic fish stocks are importantwith respect to calculations of abundance indices, stockassessment model structures, and management strategies(cf. Walters and Martell 2004).
Young North Pacific albacore (Thunnus alalunga) areknown to migrate into the cooler waters of the North PacificTransition Zone (35–458N) before moving into subtropicalwaters to spawn. Otsu and Uchida (1963) outlined possible
seasonal migration patterns of young North Pacific albacorefrom a few conventional tagging experiments and catch pat-terns of various fisheries (Fig. 1a). They showed two circu-lar migration trajectories for young North Pacific albacore:one between the western and central Pacific, and the otherbetween the eastern and central Pacific. They hypothesizedthat the two groups of northern albacore congregate andmix in the central Pacific during autumn and winter.
Intensive tagging experiments, conducted by the US, onlypartially support the migration patterns suggested by Otsuand Uchida (1963). The US North Pacific tagging experi-ments showed that albacore migrate between areas near theUS west coast and areas in the central Pacific (cf. Laurs andLynn 1977). However, although growth and exploitationrates have been estimated using these tagging data (Laursand Wetherall 1981; Bertignac et al. 1999), the previous
Received 21 July 2007. Accepted 22 April 2008. Published on the NRC Research Press Web site at cjfas.nrc.ca on 16 July 2008.J20104
M. Ichinokawa1 and Y. Takeuchi. National Research Institute of Far Seas Fisheries, Fisheries Research Agency, 5-7-1 Orido, Shimizu-ku, Shizuoka 424-8633, Japan.A.L. Coan, Jr. Southwest Fisheries Science Center, 8604 La Jolla Shores Drive, La Jolla, CA 92037, USA.
1Corresponding author (e-mail: [email protected]).
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studies have found it difficult to quantify the frequency ofalbacore migrations because of inadequate amounts of tagreleases in the western Pacific.
Japan also conducted albacore tagging experiments in thewestern areas of the North Pacific during the same period asthe US (Kikawa et al. 1977). The data from the experimentsby Japan only partially supported the migration hypothesisof Otsu and Uchida (1963). The study also suggested thenecessity for comparative analysis of the tagging data fromthe US and Japan; however, tagging data from both experi-ments have never been fully analyzed. The additional Japa-nese tag releases from the western North Pacific wouldcomplete quantitative migration information for youngNorth Pacific albacore and enable mathematical verificationof the hypothesis by Otsu and Uchida (1963).
This study estimates seasonal migration of North Pacificalbacore based on conventional tagging experiments con-ducted by Japan and the US. Results of tagging experimentsconducted during 1970s and 1980s by Japan and US aresummarized. Maximum likelihood methods are used on thetagging data and applied in a spatially structured populationdynamics model to estimate albacore seasonal migrationrates. The accuracy of these estimated migration rates andother types of information needed to sufficiently estimateNorth Pacific albacore migration rates are also discussed.
Materials and methods
Conventional tagging experiments for North Pacificalbacore
Although Japanese North Pacific albacore tag release datadate back to 1957, this study only uses data for the period
from 1971 to 1986, the period in which both US and Japa-nese tagging programs were active. The US tagging data arearchived at the Southwest Fisheries Science Center of theUS National Marine Fisheries Service (NMFS), and the Jap-anese data are archived at the National Research Institute ofFar Seas Fisheries of Japan (NRIFSF). The tagged albacorewere released mainly from US and Japanese pole-and-linevessels. A limited number of US releases were also fromvessels using jig fishing gear. Japanese pole-and-line fish-eries during 1971 to 1986 generally caught albacore lessthan 90 cm in fork length from spring through early summerin the western Pacific north of 208N (Fisheries and OceansCanada 2004). US pole-and-line fisheries also caught fishless than 90 cm mainly in the North Pacific near the USwest coast in July to October (Childers and Aalbers 2006).Because North Pacific albacore mature at ages greater than~5 years old or larger than 90 cm (International ScientificCommittee on Tuna and Tuna-like Species in the North Pa-cific Ocean (ISC) 2005), tag movement patterns should rep-resent the movement of young (immature) albacore.
The US tagging program was conducted jointly by theNMFS and the albacore fishing industry through the Ameri-can Fishermen’s Research Foundation (AFRF). Albacore forthe US tagging program were caught by commercial jigboats and pole-and-line vessels on charter to the AFRF. TheJapanese tagging program was conducted by several insti-tutes of Japan such as the Tohoku Regional Fisheries Re-search Laboratory and Nankai Regional Fisheries ResearchLaboratory, as well as NRIFSF. In the Japanese tagging pro-gram, albacore were caught by training vessels, NIRFSF re-search vessels, and chartered pole-and-line vessels. Only fishin very good condition were tagged and released in both tag-
Fig. 1. (a) Seasonal migration patterns of young (<<5-year-old) albacore, Thunnus alalunga, hypothesized by Otsu and Uchida (1963) and(b) geographic distributions of major North Pacific albacore fisheries (redrawing of fig. 1 in Bertignac et al. (1999)). Four oceanic regions inthe North Pacific (WP, CP, EP1, and EP2) used in this study to estimate migration among areas are also shown.
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Table 1. Number of (a) tag releases and (b) recoveries in area and quarter.
WP CP EP1 EP2
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
(a) Number of tag releases1971 887 8871972 289 246 1109 730 2 3741973 239 21 168 1590 14 12 2 0441974 509 812 9 1374 194 92 2 9901975 642 2 3 293 379 609 41 19691976 561 25 2 523 368 567 17 2 0631977 260 134 10 4 343 1 19 1244 273 2 2881978 321 247 120 195 259 5 63 41 1896 227 3 3741979 804 118 2 51 440 500 14 481 2 4101980 1070 38 124 214 514 32 137 19 2 1481981 300 10 100 56 89 5551982 25 10 2 168 665 214 11 3 112 23 1 2331983 40 31 5 445 73 1 262 18 218 14 1 1071984 10 558 119 235 4 28 4 9581985 13 10 422 18 230 2 23 83 8011986 494 22 485 9 112 1 122Total 0 5073 603 137 12 2469 1814 11 0 26 4930 78 68 4979 6514 1609 28 323
(b) Number of tag recoveries1972 1 3 53 19 761973 1 18 2 2 11 1 30 42 1071974 34 4 1 47 12 14 9 1211975 2 55 9 1 48 1 13 11 1401976 5 58 4 27 2 2 11 2 1 1 55 23 1911977 2 14 1 5 1 1 1 64 32 1211978 2 19 16 15 1 4 2 1 2 1 48 22 1331979 3 31 20 11 1 11 7 2 4 1 37 21 1491980 5 41 16 11 9 3 1 2 19 11 1181981 6 47 3 6 1 2 11 3 9 2 4 4 981982 9 31 2 9 1 1 1 1 2 3 1 611983 2 24 2 4 3 1 2 1 2 1 421984 16 1 2 2 1 2 6 301985 3 17 5 1 1 2 1 301986 3 13 2 3 1 1 1 241987 3 6 2 111988 1 2 1 41989 1 1 2Total 48 428 82 100 8 9 39 13 0 0 137 18 22 6 351 197 1 458
Note: Q1, January–March; Q2, April–June; Q3, July–September; Q4, October–December.
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ging programs. Both programs used spaghetti-dart-type tagswith 12–13 cm lengths of yellow polyvinyl tubing and a darthead made by Floy Tag and Manufacturing Inc. (Seattle,Washington). Further details of the US tagging program andresults of tag analyses are described by Laurs et al. (1976),Laurs and Lynn (1977), Laurs and Wetherall (1981), andBertignac et al. (1999). A brief summary of the Japanesetagging program is described in Kikawa et al. (1977).
Albacore tags were released in the Pacific between308N to 508N and recaptured mainly by US and Japanesepole-and-line fisheries operating in the same area. Tag re-lease and recapture data were grouped by four areas (WP,CP, EP1, and EP2; Fig. 1) and quarters (Q1, January–March; Q2, April–June; Q3, July–September; Q4,October–December). Young albacore recruit to the pole-and-line fisheries at approximately 2 years of age andmainly in the areas WP and EP2.
The total number of tags released during 1971 to 1986was 28 323, and the total corresponding recoveries was1458, which produces an overall recovery rate of 5.1%(Table 1). Recovery rates for Japan and the US tagging pro-grams were 6.5% and 4.9%, respectively. Most of albacoretags released in area WP were from Japan, and most of tagsreleased in areas CP, EP1, and EP2 were from the US.Twice the number of tags was released in the 1970s as inthe 1980s. The number of tag returns in 1980s was also lessthan in the 1970s. Days at liberty of the tags was <1 year in43% and 1–2 years in 38% of the total recoveries.
Tag releases and recoveries concentrated in certain years,
quarters, and area strata, so some strata had very few re-leases and recoveries (Table 1). The largest number of tagswere released in EP2, Q3, and recovered in WP, Q2. Therewere very few tag releases in Q1 for all regions, and therewere no tag returns at EP1 in Q1 and Q2. In addition, a fewthousand tags were released in Q2 and Q3 in area CP and inQ3 in area WP that were never recaptured.
Because most of tags were released and recaptured bypole-and-line fisheries (Fig. 2), the distribution of the alba-core tag releases and recaptures between area–quarter strataare very dependent on the US and Japanese pole-and-linefishing seasons. Japanese pole-and-line fisheries operatemainly in the western Pacific during the second quarter ofthe year, and US fisheries operate mainly in the easternPacific during the third quarter (Fig. 1b). Consequently, thetag releases and recaptures were concentrated mainly at Q2in the western Pacific and at Q3 in the eastern Pacific.
Movement patterns of the albacore tags between the fourareas are summarized (Table 2). The patterns suggest thatwestward movements were more frequent than eastwardmovements. For example, of the 4306 tags released in areaCP, 114 were recaptured in area WP, whereas only 11, 4,and 0 were recaptured in areas CP, EP1, and EP2, respec-tively. In addition, 119 tags released in area EP1 were re-captured in area WP, whereas only three tags released inarea WP were recaptured in area EP1.
Tag dynamics model and likelihood functionA spatially structured tag dynamics model (Hilborn 1990;
Fig. 2. Number of tag recoveries by month and gear.
Table 2. Number of tag recoveries by areas where the tags were releasedand recovered.
Recovered area
Releasedarea WP CP EP1 EP2
Total no. ofrecoveries
Total no. ofreleases
WP 330 32 3 2 367 5 813CP 114 11 4 0 129 4 306EP1 119 6 39 20 184 5 034EP2 95 20 109 554 778 13 170
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Xiao 1996) was used to describe the dynamics of the taggedgroup of albacore. Although the original equation byHilborn (1990) did not explicitly include parameters relatedto natural mortality and tag loss, we modified the equationby including a term for natural mortality and instantaneoustag-shedding rates according to Xiao (1996). In this study,tagged groups of albacore were defined as albacore releasedin the same area, quarter, and year. The total number ofalbacore (Niat) of tag group i in area a and time t is shownin eq. 1:
ð1Þ Niatþ1 ¼Xn
j¼1
½Nijtð1� hjtÞpjas�exp ð�M � Þ þ Tiat
where n is the total number of tag groups, M is the instanta-neous natural mortality rate per quarter, hat is the exploita-tion rate in area a and time t, pjas is the migration rate fromarea j to area a in season s, Tiat is the number of tagsreleased in tag group i in area a and time t, C is the instan-taneous tag-shedding rate. The parameter C is assumedconstant at 0.0245 based on an analysis that used double-tagging data for North Pacific albacore (Laurs et al. 1976).Japanese tagging data were assumed to have the same in-stantaneous tag-shedding rate, as both programs used thesame type of tag.
The exploitation rate hat in eq. 1 is modeled as the prod-uct of catchability by area and season (qsa) and year (qy)(eq. 2):
ð2Þ hat ¼ qsaqy
Hilborn (1990) and Xiao (1996) used fishing effort data toestimate hat. However, because there are no reliable histori-
cal spatially stratified effort data for the North Pacific alba-core fishery in pole-and-line fisheries, the exploitation ratesin this study were estimated with the multiplicative vectorof the catchability of qsa and qy. Note that this equation im-plicitly assumed that the relative ratio of exploitation ratesbetween seasons and areas was constant through the wholeperiod.
The expected number of tag recoveries in stratum iat(bRiat) was calculated from the estimated number of taggedfish in stratum iat (bNiat) (eq. 3):
ð3Þ bRiat ¼ bN iathatx
The parameter x is the tag-return rate of the proportion oftags returned after tag shedding. The actual value of thetag-recovery rate is unclear, but Bertignac et al. (1999) se-parate the tag-recovery rates into three components: propor-tions of tags returned after immediate tag shedding hasoccurred (1 – a), returned after capture (b), and returnedwith complete information (g). The parameter g was esti-mated as 0.88 from tag-return data. The other constants ofa and b were assumed to be 0.12 and 0.9, respectively, ac-cording to Bertignac et al. (1999). The tag-return rate of 0.7was tentatively applied to base case. However, because thenuisance parameter is difficult to estimate but important inthe estimation of some parameters, sensitivity tests wererun on a range of tag return rates between 0.5 and 0.9 totest robustness. In addition, to consider different tag-recov-ery rates, especially reporting rates of Japan and US fisher-men, we also conducted other sensitivity analyses assumingdifferent tag-recovery rates between areas EP1 and EP2 andareas CP1 and WP. In summary, we estimated the para-meters of M, pijs, qsa, and qy and assumed constant x and C.
Fig. 3. Probability distribution of the estimated migration rates calculated from the Markov chain Monte Carlo (MCMC). Black and shadedbroken lines represent the results from the fully parameterized model with assumed tag-return rates of 0.7 and 0.5, respectively. Black andshaded solid lines represent the results from the simple parsimonious model with the rates of 0.7 and 0.5, respectively, although the twolines were almost overlapped and hardly seen. Because the fully parameterized model MCMC did not converge sufficiently, the distributionpatterns shown here were only examples. The simple parsimonious model MCMC did converge. Migration routes assumed to be zero areoutlined with broken lines and migration routes hypothesized by Otsu and Uchida (1963) are outlined with thick shaded lines.
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This study assumes seasonally different albacore migra-tion rates (pijs from areas i to j in season s), which occurbetween quarters and adjacent ocean areas. Migration pat-terns are assumed to change by season, not between years.Assuming that tagged albacore can only move to adjacent
areas from a current area in a season, the transition matrixof the movement in season s (Ps) can be written as follows.The number of 1, 2, 3, and 4 corresponds to areas WP, CP,EP1, and EP2, respectively.
ð4Þ Ps ¼
1� p12s p12s 0 0
p21s 1� p21s � p23s � p24s p23s p24s
0 p32s 1� p32s � p34s p34s
0 p42s p43s 1� p42s � p43s
0BB@
1CCA
This transition matrix indicates that 32 (8 � 4) parametersof pijs should be estimated if different migration rates are as-sumed in different seasons. Considering 32 other parameterssuch as catchability (qsa, qy) and natural mortality rates (M),the total number of parameters to be estimated in this modelwas 64. The model with 64 parameters is referred to as a‘‘fully parameterized’’, or simply ‘‘full’’, model in thisstudy.
The number of expected recoveries (bRiat) in each stratumis assumed to have a Poisson distribution (Hilborn 1990;Xiao 1996):
ð5Þ LðRiatjbRiatÞ ¼exp ð�bRiatÞbRRiat
iat
Riat!
where Riat is the observed number of tag returns. The totallikelihood function for all observed recoveries against ex-pected recoveries is then
ð6Þ LðRiatjqsa; qy; pijs;MÞ ¼Y
i
Ya
Yt
exp ð�bRiatÞbRRiat
iat
Riat!
The negative log of the likelihood function (eq. 6) was
Table 3. Maximum likelihood estimates (MLE) and 90% confidence limits of the estimates under different assumptions of tag-recovery rates (x).
x = 0.70 (–1696.6) x = 0.50 (–1696.9) x = 0.50 only in EP (–1697.02)
MLE 5% lower 5% upper MLE 5% lower 5% upper MLE 5% lower 5% upperNatural mortality rates
(per quarter)0.122 0.104 0.134 0.117 0.100 0.130 0.119 0.115 0.128
Exploitation rates in 1981 (%)WP 20.7 15.6 28.4 27.7 20.3 35.8 20.7 14.8 28.3CP 2.7 1.5 5.8 3.8 2.1 8.0 2.7 1.3 6.1EP1 2.4 1.7 3.2 3.4 2.5 4.4 3.4 2.5 4.5EP2 11.8 9.7 14.2 16.3 13.4 20.0 16.4 13.4 21.1
Migration rates from Q1 to Q2CP ? EP1 0.0 0.0 21.7 0.0 0.0 17.7 0.0 0.0 16.7CP ? EP2 14.1 1.2 46.3 14.1 0.3 38.5 14.0 0.6 45.6EP1 ? CP 0.0 0.0 6.4 0.0 0.0 5.9 0.0 0.0 6.8
Migration rates from Q2 to Q3WP ? CP 89.2 83.8 93.3 88.9 80.9 93.2 89.4 84.2 93.6CP ? WP 8.0 0.4 14.4 8.0 1.6 17.1 8.0 2.1 18.4CP ? EP1 11.6 3.3 29.2 11.7 1.8 30.0 11.5 2.7 32.0EP1 ? CP 0.0 0.0 7.6 0.0 0.0 8.1 0.0 0.0 8.4EP2 ? EP1 60.4 52.8 71.4 60.3 53.4 68.2 60.3 52.4 68.6
Migration rates from Q3 to Q4WP ? CP 0.0 0.0 18.2 0.0 0.0 9.9 0.0 0.0 28.6CP ? EP1 1.5 0.0 3.7 1.5 0.2 4.3 1.5 0.0 3.9EP1 ? CP 42.1 31.9 51.9 41.8 32.6 48.2 42.4 34.3 49.7EP1 ? EP2 8.7 5.7 13.8 8.6 5.6 12.7 8.6 5.4 13.2
Migration rates from Q4 to Q1CP ? WP 92.3 84.7 96.4 92.3 84.4 96.6 92.4 83.5 96.2CP ? EP1 0.0 0.0 1.1 0.0 0.0 0.9 0.0 0.0 1.2EP1? CP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0EP1? EP2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Note: The negative log likelihood is given in parentheses after the x value in the table heading. The 90% confidence limits were calculated withthe Markov chain Monte Carlo (MCMC) method.
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minimized to estimate the parameters. The minimizationand estimation of parameters used a quasi-Newton methodin AD Model Builder (Otter Research Ltd. 2001). A conver-gence criterion of the function minimization was set to beless than 10–6 of the maximum absolute value of the vectorof derivatives of the objective function. In addition, severalsets of initial values were tried to test the global conver-gence of the solutions. Estimated confidence intervals ofthe parameters were calculated through a Markov chainMonte Carlo (MCMC) chain of 1 000 000 iterations thatsampled parameters every 1000 iterations.
Results
Model convergence and re-parameterization withassumed migration routes
In the fully parameterized model, the observed solutionsshowed a high dependency on sets of initial values and theassumed tag-return rates, even when each calculation ob-tained a maximum gradient value smaller than 10–6. Param-eter distributions for migration rates calculated by theMCMC (Fig. 3) clearly show the situation of unstability ofthe estimated parameters. Some estimated density distribu-tions have multiple local peaks at 0% and 100%, e.g., themigration rates from EP1 to EP2 and EP1 to CP in Q1. Inaddition, most migration rates, such as those from EP2 toCP through the year, were 0%. Furthermore, the shape ofthe density distribution depended on the assumed tag-returnrates. The results show that multiple local minimums in thelikelihood function of the fully parameterized model exist
and that the estimated parameters from the full model werenot actual maximum likelihood estimates.
An alternative, simpler parsimonious model with fewermigration parameters was considered. In this model, somemigration rates were previously assumed to be zero accord-ing to the results by the fully parameterized model, andsome parameters were assumed to be nonzero (estimated)according to the hypothesized migration routes by Otsu andUchida (1963, fig. 1a). For example, because most albacorein the eastern Pacific move north during summer, a southernsummer migration rate (from EP1 to EP2 in Q2) was as-sumed to be zero, whereas northern migration rates wereestimated. We also looked at tag-recovery data with short-term recoveries in adjacent areas and quarters. Among the16 routes where migration rates were assumed to be zero,no fish were actually observed to have moved in 14 of theroutes and only one fish in two of the routes. The followingtransition matrices were developed for the simpler model. Inthe following equation, migration rates fixed at zero are rep-resented by 0. Parameters pijs represent the migration routeshypothesized by Otsu and Uchida (1963). Others are param-eters without information, which will be estimated.
P1 ¼
1 0 0 0
0 1� p231�p241 p231 p241
0 p321 1� p321 0
0 0 0 1
0BB@
1CCA
Fig. 4. (a and b) Sensitivity of the assumed tag-return rates and estimated exploitation rates in 1981 and (c and d) examples of migrationrates. Results are shown from the simple parsimonious model (a and c) and from those that assume different tag-return rates only in EP1and EP2 (b and d).
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p2 ¼
1� p122 p222 0 0
p212 1� p212 � p232 p232 0
0 p322 1� p322 0
0 0 p432 1� p432
0BB@
1CCA
p3 ¼
1� p123 p123 0 0
0 1� p233 p233 0
0 p323 1� p323 � p343 p343
0 0 0 1
0BB@
1CCA
p4 ¼
1 0 0 0
p214 1� p214 � p234 p234 0
0 p324 1� p324 � p344 p344
0 0 0 1
0BB@
1CCA
The number of parameters to be estimated in this simplemodel is 16 for migration.
The simple parsimonious model successfully convergedwith a maximum gradient smaller than 10–6 and results in aunimodal parameter distribution (Fig. 3). In addition, estima-tions from some different sets of initial values arrived at thesame solutions. This suggests that relatively good estima-tions were obtained from the simple model. The maximumlikelihood estimates and their 90% confidence intervals esti-mated from MCMC are summarized (Table 3). The esti-mated confidence intervals were relatively narrow in theroutes with high migration rates, whereas ones with lowrates such as p241 (the migration rate from CP to EP2 be-tween Q2 and Q3) and p232 (from CP to EP1 between Q2and Q3) show relatively wide confidence intervals, 1%–46% and 3%–29%, respectively.
Tag movement estimation with the simple parsimoniousmodel
Assumed tag-recovery rates and estimated exploitationrates showed negative correlations, whereas assumed tagrecovery rates and natural mortality rates showed positivecorrelations (Figs. 4a–4b). On the other hand, estimated mi-gration rates (three examples of migration rates are shown)were not affected by the assumed changes in tag-recoveryrates (Figs. 4c–4d). The different tag-recovery rates in areasEP1, EP2, WP, and CP (Figs. 4b and 4d) affected estimatesof exploitation rates and natural mortality rates in EP1 andEP2. However, exploitation rates and migration rates in WPand CP were not affected. Similar results, although notshown here, were obtained when tag-return rates werechanged only in areas WP and CP. The results of the sensi-tivity analysis indicate that the estimated migration rates,with changing tag-return rate, would be robust.
The number of tag returns estimated from the simple par-simonious model explained the observed patterns of numberof tag returns by area reasonably well (Fig. 5). However,there were small differences between observed and expectedtag returns in some years and regions. The number of ex-pected tag returns in Q3, area EP1, in 1974 and 1975 wasapproximately half of the observed tag returns, whereas ex-pected tag returns in area EP2 was about twice that observedduring the same period. During 1979–1981, there were threeunexpected peaks in returned tags in area CP, whereas the
observed number of tag returns of EP1 was much smallerthan expected during the same period. These unexpected tagreturns would be caused by not considering possible annualchanges of migration rates and (or) relative ratio of exploita-tion rates between seasons and areas in the model.
Estimated migration patterns derived from the parsimoni-ous model (Table 3) and from Otsu and Uchida (1963) aresummarized (Fig. 6). The estimated migration rates withhigh probabilities roughly correspond to the routes suggestedby Otsu and Uchida (1963). Young albacore in area WPmove to area CP during the summer and then back to areaWP in winter. Young albacore in area EP2 move to area
Fig. 5. Number of tag returns observed (solid lines with circles)and expected from the parsimonious model (shaded lines) in(a) WP, (b) CP, (c) EP1, and (d) EP2.
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EP1 during the summer, about a half (42%) of them moveto area CP during autumn to winter, and some (14%) ofthem move back to area EP2 in the spring. The probabilitiesof albacore moving to areas EP1 and EP2 from area CPwere relatively low compared with those that moved to areaCP. According to these patterns, most of the new recruit-
ment (at about 2 years old) into pole-and-line fisheries inarea EP2 was expected to gather into areas CP and WPwithin a few years (Fig. 7).
DiscussionThis study summarized the combined US and Japanese
historical albacore tagging data and estimated seasonalmigration rates of young albacore in the North Pacific. Bylimiting the number of parameters and presuming rough sea-sonal migration routes, reasonable migration rates could besuccessfully estimated for young North Pacific albacore.The estimated migration patterns qualitatively correspond tothose of the previous study by Otsu and Uchida (1963) andalso suggest the possibility of frequent westward movementand infrequent eastward movement of young North Pacificalbacore.
The frequent westward movement reflects a part of thelife history migration pattern of North Pacific albacore. Theyoungest albacore generally appear in the pole-and-line fish-
Fig. 6. Movement patterns based on the estimated seasonal movement rates of young albacore, Thunnus alalunga, in the North Pacific. Theblack arrows show predicted movement patterns and rates in this study, and the shaded broken arrows show hypothesized patterns by Otsuand Uchida (1963), as shown in Fig. 1.
Fig. 7. Predicted probabilities that 2-year-old albacore, Thunnusalalunga, in area (a) WP and (b) EP2 will move into the otherareas: WP, solid bars; CP, open bars; EP1, shaded bars; EP2, diag-onally hatched bars. The probabilities were calculated from the es-timated migration rates in Table 3. In this calculation, depression ofalbacore caused from fishing activities, the intensity of whichwould be different among the areas, and natural mortality were notconsidered.
Fig. 8. Estimated exploitation rates by area and year and estimated90% confidence intervals (vertical lines). The broken line startingin 1975 (described in the legend as VPA, virtual population analy-sis) represents exploitation rates of 2- to 4-year-old North Pacificalbacore, Thunnus alalunga, calculated from numbers and catch atage of 2- to 4-year-old albacore estimated from tuning of the VPAused in the North Pacific albacore stock assessment (Fisheries andOceans Canada 2004).
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eries in areas WP and EP2, where they grow and eventuallyspawn in the subtropical region in the north-central andwestern Pacific (ISC 2005). The estimated migration ratesalso suggested that new recruits move into EP2 at 2 yearsold and gradually move to the north-central and westernPacific near their spawning ground until they are 5 yearsold. Considering the fact that mature age is approximately5 years old in North Pacific albacore (Fisheries and OceansCanada 2004), this westward movement pattern of the youngalbacore might be a reasonable part of their life history.Qualitative descriptions about seasonal migrations of NorthPacific albacore were made from a limited amount of tagdata (Otsu and Uchida 1963; Laurs and Lynn 1977). Thisstudy quantified these qualitative descriptions and revealedyoung stage albacore movement patterns.
There are still many uncertainties in the estimated param-eters, especially in exploitation rates, which directly dependon tag-return rates, and migration routes where low migra-tion rates were estimated. The simple parsimonious modelestimated higher exploitation rates in areas WP and EP2than in areas CP and EP1 (Fig. 8). The exploitation rates inarea WP were very similar to those of 2- to 4-year-old fishestimated from virtual population analyses (VPA; Fisheriesand Oceans Canada 2004). The decreasing trend of the ex-ploitation rates in the area WP was also roughly the sameas trends produced by Fisheries and Oceans Canada (2004).However, exploitation rates estimated in this study declinedmore rapidly. Absolute values of exploitation rates should becompared more carefully because of their dependency on theassumed nuisance parameter of tag-return rates. This prob-lem in estimating fishing and natural mortality rates wasalso suggested by Bertignac et al. (1999). Additional infor-mation about fishing effort should be used to estimate moreaccurate harvest rates and to stabilize our model structurefor estimating migration rates. However, reliable historicalfishing effort data stratified by area, season, and fisheriesare currently not available, and rough approximations ofhistorical trends in fishing effort may be obtained from stockassessments and used to refine harvest rates in future tagstudies.
Consideration of other model structure would also beuseful. Some extensions of the multiyear tag-recoverymodel from Brownie and Pollock (1985), which predictsrecapture by tag groups, allow explicit inclusion of age-dependant effects on survival and movement parameters(cf. Brownie et al. 1993; Schwarz et al. 1993), althoughit certainly requires more data. Further accumulation ofthe tag data would make it possible to apply the modeland estimate more stratified parameters by age and taggroups. Further studies aimed at removing or estimatingthe effects of tag-shedding rates (cf. Hearn et al. (1999)in the context of multiyear tagging model and Shirakiharaand Kitada (2004) in two tag-release – one recovery ex-periments) are also needed for accurate estimates of ex-ploitation rates and natural mortality rates from thesetagging data.
This study investigated problems in reliably estimatingseasonal migration rates with spatially and seasonallyheterogeneously distributed tag data and ways to avoid theseproblems. Although the uneven distribution of the albacoretagging data prevented us from estimating migration patterns
of young albacore in the fully parameterized model, thisstudy demonstrated that partially quantitative estimation ofmigration rates could be obtained if we chose a range of mi-gration pattern by setting up a rough migration path ofNorth Pacific albacore on the basis of previous qualitativework by Otsu and Uchida (1963) and preliminary observa-tion of the results with a fully parameterized model.Although our method would be very simplistic, the resultsshow a good example of how to incorporate qualitative (oldliterature by Otsu and Uchida 1963) and quantitative (con-ventional tag experiment) knowledge and avoid the prob-lems caused by fishery-dependent tag-release and recoverydata. For future work, it would be very useful if we couldintegrate any qualitative and quantitative knowledge onmigration, e.g., archival and conventional tagging data (Si-bert and Fournier 2001), distribution of catch data (Kimuraet al. 1997), and oceanographic conditions (Polovina et al.2001), to delimit the migration hypotheses in a more effec-tive manner.
AcknowledgementsThe authors thank all of the scientific staff and crew who
provided and complied the North Pacific albacore conven-tional tagging data. The authors also thank Hiroyuki Kurota(National Research Institute of Far Seas Fisheries) for his use-ful comments and all members of the ISC Albacore WorkingGroup, who provided many useful scientific comments.
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