ICES Journal of Marine Science, 62: 1475e1482 (2005)
doi:10.1016/j.icesjms.2005.05.014
Analysis of juvenile North Atlantic albacore (Thunnus alalunga)
catch per unit effort by surface gears in relation to
environmental variables
Nicolas Goñi and Haritz Arrizabalaga
Goñi, N., and Arrizabalaga, H. 2005. Analysis of juvenile North Atlantic albacore (Thunnus
alalunga) catch per unit effort by surface gears in relation to environmental variables. e
ICES Journal of Marine Science, 62: 1475e1482.
The relationship between the catch per unit effort (cpue) of trolling and baitboat fisheries
targeting juvenile North Atlantic albacore (Thunnus alalunga, Bonnaterre, 1788) and
several environmental variables was studied. A multiple linear regression and a generalized
least squares model (GLS) showed a significant negative relationship between age 2 albacore trolling and baitboat cpue, and the average agitation of the sea and the duration of
insolation. No clear relationship was found between the juvenile albacore cpue and sea surface temperature, precipitation, and NAO or Gulf Stream Index. Underlying processes that
could explain the negative effect of agitation and insolation are discussed, especially relating to the habitat of age 2 albacore above the seasonal thermocline. Results highlight the
necessity of considering environmental variables in the standardization of albacore cpue series used for calibrating age-structured stock assessments.
Ó 2005 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.
Keywords: albacore, baitboat, cpue, juvenile, North Atlantic, thermocline, trolling.
Received 17 January 2005; accepted 24 May 2005.
N. Goñi and H. Arrizabalaga: AZTI Fundazioa, Herrera kaia e Portualdea z/g, 20110
Pasaia, Gipuzkoa, Spain. Correspondence to N. Goñi: tel: C34 943 004800; fax: C34
943 004801; e-mail: [email protected].
Introduction
Albacore tuna (Thunnus alalunga) is a highly migratory
species, found in both subtropical and temperate waters
of the three oceans. North Atlantic albacore spawn during
summer in tropical waters around the Sargasso Sea and adjacent waters (Bard, 1981). The central Atlantic is the wintering area, and the trophic migration of juveniles (ages
1e4) occurs in summer to productive zones in the Bay of
Biscay and surrounding waters, while adults make a return
spawning migration. Juvenile albacore are exploited by surface gears from June through October during their trophic
migration to Northeast Atlantic waters. Catches are taken
mainly by Spanish trolling and baitboat gears, which delivered more than 50% of the total catch in the last decade.
Age 2 albacore are the most important age group by weight
in the catch. Adults are caught in deeper waters of the tropical Atlantic mainly by Chinese Taipei and Japanese longline fisheries that represent nearly 20% of the catch in the
last decade (ICCAT, 2001). The International Commission
for the Conservation of Atlantic Tunas (ICCAT) assesses
the stock, using a virtual population analysis (VPA) model
1054-3139/$30.00
tuned with different standardized trolling and longline cpue
series as relative abundance indices. The cpue series are
standardized using general linear models (GLM to account
for seasonal and spatial variability not related to
abundance).
Cpue trends are proportional to abundance only if catchability remains constant. However, environmental effects
on fish distribution and/or behaviour can often influence
catchability. Consequently, standardized cpues can be biased if these environmental effects are not properly taken
into account during the standardization process (Fréon
and Misund, 1999). In the case of North Atlantic albacore,
indices obtained for VPA tuning have not considered environmental influence on catchability.
In 1996, an ICCAT Tuna Symposium identified research
about relationships between tuna population dynamics, fisheries, and variability of the environment related to tuna habitat as being important (ICCAT, 1998). Since then,
knowledge about environmental influence on tuna distribution or behaviour has been improved using either electronic
tagging (Block et al., 1997; Brill et al., 1999; Lutcavage
et al., 2000; Brill et al., 2002; Itoh et al., 2003; Musyl
Ó 2005 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.
1476
N. Goñi and H. Arrizabalaga
et al., 2003; Kitagawa et al., 2004), time-series (Ravier and
Fromentin, 2004), or spatial analysis (Maury et al., 2001,
Royer et al., 2004). Enhancement of empirical knowledge
allowed the modelling of tuna behaviour and distribution in
relation to the environment (Bertignac et al., 1998; Humston
et al., 2000; Lehodey et al., 2003), and very recently,
habitat-based models were used to estimate effective longline fishing effort and realistic relative abundance series for
some tropical tunas (Bigelow et al., 2002, 2003). In spite of
this, knowledge about albacore behaviour and catchability
in relation to environment is scarce and limited to the adult
fraction of the population in tropical waters (Bertrand,
1999; Bertrand and Josse, 2000). Although trolling cpue series are used as juvenile albacore abundance indices in the
North Atlantic, little is known about their horizontal and
vertical distribution pattern and the environmental effects
on this behaviour and, consequently, on catchability by surface gears such as trolling or baitboat.
During the 2000 North Atlantic albacore stock assessment session, it was noticed that the age 2 Spanish trolling
cpue series showed contradictory information with respect
to other abundance indices (ICCAT, 2001). This could be
explained by environmental influences affecting the catchability by surface gears that are not taken into account during the standardization process. Therefore, the aim of the
present study is to look for statistical relationships between
the annual catch per unit effort of juvenile (ages 1e4) albacore caught by trolling and baitboat gears and several environmental variables as well as abundance estimated from
VPA analysis.
Material and methods
Environmental variables
Four local and two broader-scale climatic variables were
used. The local ones, all measured on the Basque coast
and provided by the Météo-France station in Biarritz,
France, were sea surface temperature (SST) in (C (provided
by the Aquarium of San Sebastian, Spain), insolation (defined as the number of hours of solar exposure), precipitation in mm, and average sea agitation or sea state
(S code). The S code, accepted by the World Meteorological Organisation in 1970, is a nine-level scale describing
the state of the sea, derived from the Beaufort scale (Vagnon and Wadous, 1993).
Variability of SST, measured at one point, is considered
here as an index of the latitudinal variability of the SSTrange of the juvenile albacore habitat in the North Atlantic.
Insolation indexes the mean luminosity of the water column
during the fishing season, which can influence the vertical
distribution of juvenile albacore or their prey. Agitation
of the sea determines the thermal vertical structure of the
water column, in particular the depth of the mixed layer,
which can also influence juvenile albacore vertical distribution. Precipitation indexes river discharge into coastal
zones, which can enhance the productivity of some fish
and invertebrate stocks (Lloret et al., 2001) that juvenile albacore use as prey. For all variables, monthly (June through
October) average values derived from daily observations
were used. Although the Basque coast is a relatively marginal zone area for Northeast Atlantic trolling and baitboat
fishing, coastal measurements are positively correlated with
open water measurements (Pérez et al., 2000; Valencia
et al., 2003). Moreover, trolling and baitboat fisheries usually operate from June to the end of October, so we assume
that trends in climatic variables measured on the Basque
coast reflect trends in climatic conditions of the albacore
trolling and baitboat fishing areas in the Northeast Atlantic.
The broader-scale climatic variables considered are the
Gulf Stream Index (GSI, provided by the Plymouth Marine
Laboratory) and the winter (December through March) values of the North Atlantic Oscillation (NAO, provided by
the University of East Anglia, based on the work of Jones
et al., 1997). The GSI, calculated by Taylor (1996), indexes
the latitude of the Gulf Stream North wall. Changes in the
index have been correlated with shifts in wind, temperature,
and atmospheric pressure and salinity patterns across the
North Atlantic and in western Europe. The winter NAO
is correlated with summer North Atlantic SST anomalies,
which could induce changes in catchability of Spanish
and French surface fisheries that account for the majority
of the catch (Bard, 2001b).
In addition to these abiotic environmental variables, annual average albacore abundance-at-age is considered as
a biotic explanatory variable.
Calculation of abundance-at-age
With the aim of using average abundance values from the
VPA as an explanatory variable for albacore cpue by trolling and baitboat gears, it would have been redundant to include the trolling cpue series in the VPA tuning (as done in
ICCAT, 2001). In order to avoid biased average abundance
values, the albacore VPA was tuned using longline abundance indices only.
The following cpue series were used as abundance indices, from the 2000 ICCAT stock assessment, and are taken
as a reference for the present study: Spanish trolling (ages 2
and 3), French trolling (ages 2 and 3), Chinese Taipei longline (ages 2 through 8C), Japanese longline (ages 3
through 8C), and US longline (ages 3 through 8C) (see
Table 1 for details; ICCAT, 2001). In our study, we chose
not to use the US longline cpue series, as it represents, on
average, only 0.41% of the yearly catches and shows high
interannual variability, which is unlikely to reflect the
trends in stock abundance. In addition, the Chinese Taipei
longline cpue series was separated into two periods
(1975e1986 and 1987e1998), because in 1987, the Taipei
fleet stopped targeting albacore and started targeting bigeye
and bluefin tuna (Hsu, 1999). Albacore was mainly bycatch
in the second period.
Juvenile North Atlantic albacore cpue by surface gears, and environmental variables
Table 1. Standardized cpue series used as abundance indices in the
North Atlantic albacore stock assessment (* indicates indices used
in our VPA model).
Index
Age
range
1.
2
2.
3
3.
2e3
4.
2e3
5.*
3e8C
6.*
2e8C
7.*
2e8C
8.
3e8C
Fishery
Years
Spanish
trolling
Spanish
trolling
French
trolling
French
trolling
Japanese
longline
Chinese
Taipei
longline
Chinese
Taipei
longline
US
longline
1981e1999
1477
Cpue data
Standardized cpue data for ages 1e4 for the Spanish trolling fishery targeting albacore are provided by Ortiz de
Zarate et al. (2001) and Ortiz de Zarate and Cramer (2001).
Reference
Statistical analyses
1981e1999
1975e1979
1980e1986
1975e1999
(Ortiz de Zarate
et al., 2001)
(Ortiz de Zarate
et al., 2001)
(Goujon
et al., 1996)
(Goujon
et al., 1996)
(Uosaki, 2001)
1975e1986
(Tzeng
et al., 2001)
1987e1998
(Tzeng
et al., 2001)
1982e1999
(Ortiz and Cramer,
2001)
Multiple linear regressions were used to look for relationships among albacore cpue and trolling, baitboat, and environmental variables. As given by Gulland (1983), cpue can
be defined as the product between catchability and average
abundance:
g
cpuet ZqNt ee
ð2Þ
where cpuet is the catch per unit effort at time t with a lognormal error term, q the catchability coefficient (in t1), Nt
the average abundance at time t, g a shape parameter (Harley et al., 2001), and e is the error term. In our case, we assume that part of the residual variability in this model can
be explained by environmental variables, so the full model
on logarithmic scale can be rewritten as:
lnðcpuet ÞZlnðqÞCg lnðNt ÞC
n
X
ai Xi Ce
ð3Þ
iZ1
Following the ICCAT approach (2001), selectivity at age
for the indices that reflect the abundance of a group of ages
was estimated from the partial catches of those fleets (Butterworth and Geromont, 1999). Terminal fishing mortality
rates for ages 2e7 were estimated, and the 1999 fishing
mortality rate for age 1 was set to 20% of that for age 2.
The f-ratio (f of the oldest age divided by the f of the next
younger age) was fixed at 1.0 for all years, and the natural
mortality rate was fixed at 0.3. Software VPA-2box (Porch
et al., 2001) was used for tuning the VPA. In order to assess
the adequacy of choosing only longline abundance indices
for tuning the VPA, the outputs of this assessment (in terms
of abundance-at-age, spawning-stock biomass, and fishing
mortality trends) and the correctness of fit of the model
to the data (given by the Akaike Information Criteria,
AIC) were compared with those of the base case stock assessment carried out in 2000 (ICCAT, 2001).
Abundance and fishing mortality estimates from the VPA
allowed us to calculate the annual average abundance of
North Atlantic albacore for each age group, according to
Gulland’s (1983) formula:
Nt NtC1
Nt Z
Ft CM
ð1Þ
For year t, Nt is the cohort size at the beginning of year t,
NtC1 the cohort size at the beginning of year t C 1, Ft the
fishing mortality during year t, and M is the natural mortality during that year.
where Xi are the environmental variables considered, and e
is normally distributed, i.e. e w N(0,s).
In the cases where the time-series showed significant autocorrelation, generalized least squares (GLS) models were
applied in addition to multiple linear regressions in order to
take this autocorrelation into account when estimating the
model coefficients and related parameters. Generalized
least squares models are linear models fitted using maximum likelihood, in which the errors are allowed to be correlated and/or have unequal variances (Carroll and Ruppert,
1988). Analyses were performed using R 1.9.0 software.
Results
In the VPA using only longline data (used here for the calculation of average abundance-at-age) as well as the base
case VPA, the index that showed the highest contribution
to the total log-likelihood value was the Japanese longline
cpue series for ages 3e8C (Table 2). The fit using only
Japanese and Chinese Taipei longline indices was more
sparing than in the base case including all indices
(AIC Z 104.9 vs. AIC Z 384.52, respectively). Accordingly,
we considered this assessment model appropriate for estimating stock abundance values to be used as explanatory
variables of trolling and baitboat cpue. In spite of this, it is
important to note that the VPA fishing mortality and abundance trends tuned only with longline abundance indices
did not differ substantially from the base case, which would
indicate that abundance estimates are not sensitive to the
choice of tuning indices.
1478
N. Goñi and H. Arrizabalaga
Table 2. Contribution of the abundance indices (columns 1e7) to the total log-likelihood value of two different assessment models, and
their correctness of fit to the data (AIC, right column). See Table 1 for index characteristics.
Abundance indices
Base case
(all indices)
Longline indices
only
1
2
3
4
5
6
7
AIC
11.67
18.76
4.53
2.29
20.21
7.09
10.71
384.52
d
d
d
d
21.81
7.2
10.55
104.9
For each of the four age groups considered, baitboat and
trolling cpue series show relatively similar patterns. Multiple linear regression models showed that age 2 albacore
cpues by both gears were significantly correlated to average
agitation of the sea and insolation (Table 3, Figure 1).
Relationships between cpue and agitation and cpue and
insolation were negative and more significant for trolling
(p Z 0.0067; p Z 0.0004, respectively) than for baitboat
(p Z 0.0227; p Z 0.0233, respectively). Although a positive
correlation between insolation and SST (r2 Z 0.2294,
p Z 0.0380), and a negative correlation between precipitation and insolation (r2 Z 0.6485, p Z 0.0000) and precipitation and SST (r2 Z 0.298, p Z 0.0156) were observed, this
was not a problem since, among these three variables, only
insolation was retained in the final model. No significant relationship was found between the cpue of other age groups
and the environmental variables used. We found a significant relationship between cpue and the annual average
abundance, but only for age 3 and age 4 trolling cpue series.
As the sea agitation series is significantly autocorrelated
at lag 1 (p Z 0.0020), which indicates the presence of a time
trend, the assumption of independence of observations was
not realized when using multiple linear regression. Using
autocorrelated variables in the regression model tends to
underestimate the variance parameters, and thus underestimates p-values. In this situation, a generalized least squares
model (GLS) was applied as specified in the Statistical
analyses section above. The same relationships were found
as with the multiple linear regressions, with slightly higher
p-values (Table 3). Again, the negative relationships between trolling cpue and both agitation and insolation were
more significant for trolling (p Z 0.0199; p Z 0.0004) than
the baitboat relationships. The fit of the GLS models to the
trolling and baitboat cpues was satisfactory (Figure 2). Age
2 cpue values for both gears showed a fairly constant value
during the first decade of the study period (1981e1990).
During the second decade, a dome shape in the cpue was
observed (1991e1999), with an increasing trend during
the first half of the decade followed by a decreasing trend
in the second half, reaching similar values as in the first decade at the end of the time-series.
In our case, although the use of the multiple linear
regression model would be statistically less correct than
the use of the GLS model, the residuals of both multiple linear regressions are normally distributed and are not autocorrelated at lags 1e6, and are very similar to those of the
respective GLS models (Figure 3). This indicates that the
time trends are included in the variability explained by
the multiple linear regressions.
Discussion
Although Spanish trolling and baitboat fleets targeting albacore have incorporated some technological improvements
(i.e. sonar, echosounders, etc.) during the 19 years studied
until the present, no significant effect has been documented
between nominal cpue and these technological improvements that could explain the time trends observed in cpue
through statistical modelling. Moreover, the technological
improvement of the fleet has been rather continuous, and
is not likely to explain the dome shape in the cpue trends
(Figure 2), which is probably related to environmental
variables.
The agitation of the sea metric determines the depth of
the mixed layer in summer, and thus the depth of the seasonal thermocline (Perès and Devèze, 1963). Bard (1981)
reported that age 2 albacore usually stay above this seasonal thermocline. Consequently, agitation of the sea may indirectly influence vertical availability of albacore to
surface gears: the higher the agitation, the less available
are age 2 albacore at the surface. Murray and Bailey
(1986) also reported a negative effect of thermocline depth
on albacore catchability by trolling in the South Pacific. A
similar phenomenon was also observed for yellowfin tuna
in the Gulf of Guinea (Maury et al., 2001), where within
a single year, the purse-seine cpues appeared negatively related to the thermocline depth.
Insolation may influence the prey distribution of age 2 albacore. High insolation values may lead to deeper distributions of age 2 albacore prey and, thus, of age 2 albacore
foraging on them. Marchal (1993) described the effect
of insolation on the vertical distribution of the clupeid
Sardinella aurita in the Gulf of Guinea: the higher the insolation, the deeper the distribution. Consequently, albacore
availability and vulnerability to surface gears would be
diminished. In spite of this, the fact that only age 2 cpue
Juvenile North Atlantic albacore cpue by surface gears, and environmental variables
1479
Table 3. Estimated coefficients from the multiple linear regression and GLS models applied to logarithm of age 2 albacore cpue by trolling
and baitboat.
Multiple linear regression
GLS
Coefficient
s.e.
t-Value
p-Value
Coefficient
s.e.
t-Value
p-Value
Trolling
Intercept
Agitation
Insolation
6.9554197
0.6047846
0.0018122
0.6303154
0.194357
0.0004052
11.035
3.112
4.473
6.86E09
0.006713
0.000385
6.992929
0.570321
0.001941
0.7326768
0.2205705
0.0004375
9.544358
2.585661
4.435652
0
0.0199
0.0004
Baitboat
Intercept
Agitation
Insolation
11.135412
1.441661
0.002988
1.853934
0.571658
0.001192
6.006
2.522
2.507
1.83E05
0.0227
0.0233
11.592363
1.563359
0.003145
2.3803131
0.7033933
0.0012801
4.8701
2.222596
2.456883
0.0002
0.041
0.0258
comparison to trolling, may be explained by the influence
of several parameters related to schooling (such as school
size, number, and behaviour of schools) on catchability
by baitboats, as suggested by Bard (1981). The absence
of correlation between abundance and cpues by both gears
for ages 1 and 2 might be indirectly related to their habitat
6
4.4
6
4.2
5.5
4.2
5.5
5
4.5
3.6
4
3.4
3.5
3.2
3
3
2
2.2
2.4
2.6
2.8
3
4
5
3.8
4.5
3.6
4
3.4
3.5
3.2
3
750
2.5
3.2
3
850
Sea agitation (S code)
ln(cpuetr2)
4.5
3.6
4
3.4
3.5
3.2
3
15
15.2
15.4
6
4.2
5.5
4
5
3.8
4.5
3.6
4
3.4
3.5
3.2
3
3
18.75
2.5
15.6
19
19.25 19.5 19.75
ln(cpuetr2)
4.5
3.6
4
3.4
3.5
3.2
3
2.5
3
0
winter NAO
1
2.5
20.25 20.5
2
3
4.4
6
4.2
5.5
4
5
3.8
4.5
3.6
4
3.4
3.5
3.2
ln(cpuebb2)
5
ln(cpuebb2)
4
3.8
ln(cpuetr2)
6
5.5
4.2
-1
20
SST(ºC)
4.4
-2
1150
4.4
ln [average abundance]
-3
1050
ln(cpuebb2)
5
ln(cpuebb2)
4
3.8
ln(cpuetr2)
6
5.5
4.2
14.8
950
2.5
1250
Insolation (hours)
4.4
3
14.6
ln(cpuebb2)
4
3.8
ln(cpuetr2)
4.4
ln(cpuebb2)
ln(cpuetr2)
shows a significant relationship with insolation cannot be
explained easily, since our lack of knowledge about the
specific ecological niche of each age group limits our ability to give a thorough interpretation of this result.
The absence of correlation between stock abundance and
cpue by baitboat for age 3 and age 4 albacore, in
3
2.5
3
-0.5
0.5
1.5
2.5
GSI
Figure 1. Scatter plots between the logarithm of age 2 cpue for trolling (ln(cpuetr2), left axis, black spots) and baitboat (ln(cpuebb2), right
axis, white spots) and the environmental variables used: sea agitation, insolation, logarithm of average abundance (average abundance
being expressed in number of individuals), SST (sea surface temperature), winter NAO (December through March North Atlantic Oscillation), and GSI (Gulf Stream Index).
1480
N. Goñi and H. Arrizabalaga
70
400
60
350
300
50
200
30
cpuebb2
cpuetr2
250
40
150
20
100
10
50
0
0
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Figure 2. Annual cpue estimates (diamonds) and GLS predicted annual cpues (lines) for age 2 albacore caught by trolling gear (left axis,
black diamonds, solid line) and baitboat gear (right axis, white diamonds, dotted line).
or to their behaviour, but we are unable to give a more thorough interpretation of this result given the reasons explained above.
Bard (2001b) hypothesized that variations of SST anomalies in the Northeast Atlantic, related to the winter NAO,
could modify juvenile albacore migration patterns. According to our results, neither sea surface temperature nor
ln(cpuetr2) residuals
0.4
winter NAO seems to influence cpue of albacore to surface
gears targeting juvenile albacore. A finer scale analysis in
time and space should be conducted to study this hypothesis
further. Ortiz de Zarate et al. (1998) showed a negative relationship between catches of age 2 and age 3 albacore and
the Gulf Stream Index, hypothesizing an effect of the latitude of the Gulf Stream North wall on the distribution,
a
0
-0.4
1981
ln(cpuebb2) residuals
1.5
1983
1985
1987
1989
1991
1993
1995
1997
1999
1983
1985
1987
1989
1991
1993
1995
1997
1999
b
0
-1.5
1981
Figure 3. Residuals of the multiple linear regression (crosses) and generalized least squares model (open circles) applied to logarithm of
age 2 albacore cpue for (a) trolling (ln(cpuetr2)), and (b) baitboat (ln(cpuebb2)) gears.
Juvenile North Atlantic albacore cpue by surface gears, and environmental variables
and thus on the availability, of albacore to surface fisheries.
Our results do not allow us to confirm this hypothesis, at
least in terms of cpue.
Several facts could help us to understand the lack of
relationships connecting cpue and environmental factors for
ages 1, 3, and 4. For older fish, Bard (1981) reported that age
3 and age 4 albacore use deeper ecological niches below the
seasonal thermocline with more abundant trophic resources.
Therefore, we can suppose that their distribution is less likely
to be affected directly by sea surface or atmospheric environmental variables. Information on the specific biology of age
1 albacore is more meagre. Individuals of this age group are
not completely recruited to the fishery (ICCAT, 2001), which
limits any interpretation of results based on fishery models.
The use of VPA abundance estimates (tuned only with
longline indices) as explanatory variables may be a cause
for concern. Bertrand (1999) showed that adult albacore
catchability by longline is influenced by the presence of micronekton aggregates in their habitat. Bard (2001a) also reported that adult albacore catchability by longline is
influenced by their stomach repletion. Since these trophic
parameters were not taken into account in the cpue standardization process (indeed, this is not a trivial issue), the
reliability of the longline cpue series used as abundance indices for adult albacore could be called into question. However, we believe that the distribution of prey could equally
affect catchability by a surface gear such as trolling, especially if the fishing activity takes place in a small space
and time scale, similar to the albacore trolling fishery.
Taking this into account, we can consider that longline
standardized cpue series are likely to be more reliable abundance indices than the trolling series, unless the latter were
to be properly analysed to remove variability unrelated to
abundance. Finally, it was observed that the outputs of
the VPA were not sensitive to the choice of different tuning
indices. This does not mean that other VPA assumptions
are unimportant, since they contribute to sources of uncertainty. It is important to stress that our average abundance
estimates are based on indirect methods, so relationships
between cpue and abundance observed in this study should
be considered with caution, as their validity depends on the
reliability of abundance estimates.
Overall, the present work allowed us to show the negative effects of sea agitation and insolation on age 2 albacore
cpue by trolling and baitboat gears. The possible underlying
processes have been discussed in order to interpret such relationships. Work of this kind highlights the importance of
considering environmental variables in the standardization
of cpue series used for stock assessment. Enhanced knowledge of the habitat use of juvenile albacore, e.g. its oxygen
preferences and the influence of primary production and
thermal fronts on its distribution, would enable a habitatbased model to be considered. Such a tool would be invaluable in standardizing cpue to obtain abundance indices from
seasonal surface fisheries (i.e. trolling and baitboat) affected
by environmental variability.
1481
Acknowledgements
We thank Clay Porch (US National Marine Fisheries Service, Miami, Florida) for the explanations of VPA-2box,
Chien Chung Hsu and Ching Ping Lu (Institute of Oceanography, National Taiwan University) for the information
about the Chinese Taipei longline fishery, Georges Hémery
(Muséum National d’Histoire Naturelle, Biarritz) and Bernard Dupont (Météo-France, Biarritz) for the meteorological data provided, Dorleta Garcı́a and Leire Ibaibarriaga
(AZTI Foundation) for their help in the statistical analysis,
and both anonymous referees for their comments on an earlier version of the manuscript.
References
Bard, F. X. 1981. Le thon germon (Thunnus alalunga Bonnaterre
1788) de l’Océan Atlantique. Thèse Doc. État. Univ. Paris VI.
333 pp.
Bard, F. X. 2001a. Apparent effect of stomach repletion on catchability of large tunas to longline gear. Comparison with other
fishing gears. ICCAT Collective Volume of Scientific Papers,
52: 452e465.
Bard, F. X. 2001b. Extension of the geographical and vertical habitat of albacore (T. alalunga) in the North Atlantic. Possible consequences on true rate of exploitation of this stock. ICCAT
Collective Volume of Scientific Papers, 52: 1447e1456.
Bertignac, M., Lehodey, P., and Hampton, J. 1998. A spatial population dynamic simulation model of tropical tunas using a
habitat index based on environmental parameters. Fisheries
Oceanography, 7: 326e334.
Bertrand, A. 1999. Le système thon-environnement en Polynése
Française: caractérisation de l’habitat pélagique, étude de la distribution et de la capturabilité des thons, par méthodes acoustiques et halieutiques. Thèse de l’Ecole Nationale Supérieure
Agronomique de Rennes. 295 pp.
Bertrand, A., and Josse, E. 2000. Acoustic estimation of longline
tuna abundance. ICES Journal of Marine Science, 57: 919e926.
Bigelow, K., Hampton, J., and Miyabe, N. 2002. Application of
a habitat-based model to estimate effective longline fishing effort
and relative abundance of Pacific bigeye tuna (Thunnus obesus).
Fisheries Oceanography, 11: 143e155.
Bigelow, K., Maunder, M., and Hinton, M. 2003. Comparison of
deterministic and statistical habitat-based models to estimate effective longline effort and standardized cpue for bigeye and yellowfin tuna. SCTB16 working paper, RG-3.
Block, B., Keen, J., Castillo, B., Dewar, H., Freund, E., Marcinek,
D., Brill, R., and Farwell, C. 1997. Environmental preferences of
yellowfin tuna (Thunnus albacares) at the northern extent of its
range. Marine Biology, 130: 119e132.
Brill, R., Block, B., Boggs, C., Bigelow, K., Freund, E., and Marcinek, D. 1999. Horizontal movements and depth distribution of
large adult yellowfin tuna (Thunnus albacares) near the Hawaiian Islands, recorded using ultrasonic telemetry: implications for
the physiological ecology of pelagic fishes. Marine Biology,
133: 395e408.
Brill, R., Lutcavage, M., Metzger, G., Bushnell, P., Arendt, M.,
Lucy, J., Watson, C., and Foley, D. 2002. Horizontal and vertical
movements of juvenile bluefin tuna (Thunnus thynnus), in relation to oceanographic conditions of the western North Atlantic,
determined with ultrasonic telemetry. Fishery Bulletin US,
100: 155e167.
1482
N. Goñi and H. Arrizabalaga
Butterworth, D. S., and Geromont, H. F. 1999. Some aspects of
ADAPT VPA as applied to North Atlantic bluefin tuna. ICCAT
Collective Volume of Scientific Papers, 47: 233e241.
Carroll, R. J., and Ruppert, D. 1988. Transformation and Weighting
in Regression. Chapman and Hall.
Fréon, P., and Misund, O. A. 1999. Dynamics of Pelagic Fish Distribution and Behaviour: Effects on Fisheries and Stock Assessment. Blackwell Science, London. 348 pp.
Goujon, M., Antoine, L., and Leroy, B. 1996. Captures de la pêcherie germonière Française au filet maillant dérivant dans l’Atlantique Nord-Est. ICCAT Collective Volume of Scientific Papers,
43: 191e205.
Gulland, J. A. 1983. Fish stock assessment, a manual of basic
methods. FAO/Wiley Series on Food and Agriculture, vol. 1.
233 pp.
Harley, S. J., Myers, R. A., and Dunn, A. 2001. Is catch-per-uniteffort proportional to abundance? Canadian Journal of Fisheries
and Aquatic Sciences, 58: 1760e1772.
Hsu, C. C. 1999. Simulation of age-structured leslie matrix model
to evaluate the reproductive values of the North Atlantic albacore stock. Journal of the Fisheries Society of Taiwan, 26:
59e71.
Humston, R., Hault, J. S., Lutcavage, M., and Olson, D. B. 2000.
Schooling and migration of large pelagic fishes relative to environmental cues. Fisheries Oceanography, 9: 136e146.
ICCAT. 1998. Report of the ICCAT Tuna Symposium, Ponta
Delgada-Azores, 10e18 June 1996. ICCAT Collective Volume
of Scientific Papers, 50.
ICCAT. 2001. Report of the ICCAT SCRS albacore stock assessment session (Madrid, Spain e 9e15 October 2000). ICCAT
Collective Volume of Scientific Papers, 52: 1283e1390.
Itoh, T., Tsuji, S., and Nitta, A. 2003. Swimming depth, ambient
water temperature preference, and feeding frequency of young
Pacific bluefin tuna (Thunnus orientalis) determined with archival tags. Fishery Bulletin US, 101: 535e544.
Jones, P. D., Jónsson, T., and Wheeler, D. 1997. Extension to the
North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland. International
Journal of Climatology, 17: 1433e1450.
Kitagawa, T., Kimura, S., Nakata, H., and Yamada, H. 2004. Diving behavior of immature, feeding Pacific bluefin tuna (Thunnus
thynnus orientalis) in relation to season and area: the East China
Sea and the KuroshioeOyashio transition region. Fisheries
Oceanography, 13: 161e180.
Lehodey, P., Chai, F., and Hampton, J. 2003. Modelling climaterelated variability of tuna populations from a coupled oceanbiogeochemical-populations dynamics model. Fisheries
Oceanography, 12: 483e494.
Lloret, J., Lleonart, J., Solé, I., and Fromentin, J. M. 2001. Fluctuations of landings and environmental conditions in the northwestern Mediterranean Sea. Fisheries Oceanography, 10: 33e50.
Lutcavage, M. E., Brill, R. W., Skomal, G. B., Chase, B. C., Goldstein, J. L., and Tutein, J. 2000. Tracking adult North Atlantic
bluefin tuna (Thunnus thynnus) in the northwestern Atlantic using ultrasonic telemetry. Marine Biology, 137: 347e358.
Marchal, E. 1993. Biologie et écologie des poissons pélagiques
côtiers du littoral ivoirien. In Environnement et Ressources
Aquatiques de la Côte d’Ivoire. vol. I. Le Milieu Marin,
pp. 237e269. Ed. by P. Le Loeuff, E. Marchal, and J-B. AmonKhotias. Orstom Editions, Paris.
Maury, O., Gascuel, D., Marsac, F., Fonteneau, A., and De Rosa,
A-L. 2001. Hierarchical interpretation of nonlinear relationships
linking yellowfin tuna (Thunnus albacares) distribution to the
environment in the Atlantic Ocean. Canadian Journal of Fisheries and Aquatic Sciences, 58: 458e469.
Murray, T., and Bailey, K. 1986. Preliminary report of RV Kaharoa
cruises K03/86 and K05/86 e oceanography and albacore catch
rates. Paper presented at the First South Pacific Albacore Research (SPAR) Workshop, 9e12 June 1986, Auckland, New
Zealand. South Pacific Community, WP/11.
Musyl, M., Brill, R., Boggs, C., Curran, D., Kazama, T., and Seki,
M. 2003. Vertical movements of bigeye tuna (Thunnus obesus)
associated with islands, buoys, and seamounts near the main Hawaiian Islands from archival tagging data. Fisheries Oceanography, 12: 152e169.
Ortiz, M., and Cramer, J. 2001. Standardized catch rates for albacore (Thunnus alalunga) from the U.S. Pelagic Longline fleet
1982e1999. ICCAT Collective Volume of Scientific Papers,
52: 1457e1467.
Ortiz de Zarate, V., and Cramer, J. 2001. Standardized age specific
catch rates for albacore, Thunnus alalunga, from the Spanish
baitboat fishery in the Northeast Atlantic, 1981e1999. ICCAT
Collective Volume of Scientific Papers, 52: 1418e1428.
Ortiz de Zarate, V., Cramer, J., and Ortiz, M. 2001. Standardized
age specific catch rates for albacore, Thunnus alalunga, from
the Spanish troll fishery in the Northeast Atlantic, 1981e1999.
ICCAT Collective Volume of Scientific Papers, 52: 1407e1417.
Ortiz de Zarate, V., Lavin, A., and Moreno-Ventas, X. 1998. Is
there a relationship between environmental variables and the surface catch of albacore (Thunnus alalunga, Bonnaterre, 1788) in
the North Atlantic? ICCAT Collective Volume of Scientific
Papers, 48: 250e252.
Perès, J-M., and Devèze, L. 1963Océanographie biologique et biologie marine (tome II). Presses Universitaires de France. 511 pp.
Pérez, F. F., Pollard, R. T., Read, J. F., Valencia, V., Cabanas,
J. M., and Rı́os, J. M. 2000. Climatological coupling of the thermohaline decadal changes in Central Water of the Eastern North
Atlantic. Scientia Marina, 64: 347e353.
Porch, C. E., Turner, S. C., and Powers, J. E. 2001. Virtual population analysis of Atlantic bluefin tuna with alternative models of
transatlantic migration. ICCAT Collective Volume of Scientific
Papers, 52: 1022e1045.
Ravier, C., and Fromentin, J. M. 2004. Are the long-term fluctuations in Atlantic bluefin tuna (Thunnus thynnus) population
related to environmental changes? Fisheries Oceanography, 13:
145e160.
Royer, F., Fromentin, J. M., and Gaspar, P. 2004. Association between bluefin tuna schools and oceanic features in the western
Mediterranean. Marine Ecology Progress Series, 269: 249e263.
Taylor, A. H. 1996. NortheSouth shifts of the Gulf Stream: oceaneatmosphere interactions in the North Atlantic. International
Journal of Climatology, 16: 559e583.
Tzeng, T. D., Wang, S. J., Chang, Y., and Yeh, S. Y. 2001. Standardized cpue trend of Taiwanese longline fishery for Northern
Atlantic albacore from 1968 to 1998. ICCAT Collective Volume
of Scientific Papers, 52: 1481e1489.
Uosaki, K. 2001. Updated standardized cpue for albacore caught by
Japanese longline fishery in the Atlantic Ocean, 1975e1999.
ICCAT Collective Volume of Scientific Papers, 52: 1468e1474.
Vagnon, H., and Wadous, P. 1993. Code Vagnon de la Mer. Editions du Plaisancier, Caluire, France. 191 pp.
Valencia, V., Borja, Á., Fontán, A., Pérez, F. F., and Rı́os, A. F.
2003. Temperature and salinity fluctuations along the Basque
Coast (southeastern Bay of Biscay), from 1986 to 2000, related
to climatic factors. ICES Journal of Marine Science, 219:
340e342.