ICES Journal of Marine Science (2011), 68(6), 1063–1071. doi:10.1093/icesjms/fsr045
Ocean variations associated with fishing conditions for yellowfin
tuna (Thunnus albacares) in the equatorial Atlantic Ocean
Kuo-Wei Lan 1, Ming-An Lee 1,2*, Hsueh-Jung Lu 1, Wei-Juan Shieh 1,3, Wei-Kuan Lin 1,
and Szu-Chia Kao 1
1
Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung 20224, Taiwan, ROC
Center of Excellence for Marine Bioenvironment and Biotechnology, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung 20224, Taiwan,
ROC
3
Taiwan Ocean Research Institute, National Applied Research Laboratories, 3F, 106, Ho-Ping E. Road, Section 2, Taipei 106, Taiwan, ROC
2
*Corresponding Author: tel: +886 2 24622192 ext. 5032; fax: +886 224634419; e-mail: [email protected]
Lan, K-W., Lee, M-A., Lu, H-J., Shieh, W-J., Lin, W-K., and Kao, S-C. 2011. Ocean variations associated with fishing conditions for yellowfin tuna
(Thunnus albacares) in the equatorial Atlantic Ocean. – ICES Journal of Marine Science, 68: 1063– 1071.
Received 22 May 2010; accepted 28 February 2011; advance access publication 17 May 2011.
In this study, the Taiwanese longline (LL) fishery data were divided into two types: regular LL and deep LL. Furthermore, we collected
environmental variables, such as sea surface temperature (SST), subsurface temperature, chlorophyll a concentration, net primary productivity, windspeed, and the north tropical Atlantic SST index (NTA) during the period 1998 – 2007 to investigate the relationship
between LL catch data and oceanic environmental factors using principal component analysis (PCA). After the daily LL was separated
into two types of LL, the results indicated that the deep LL was the major fishery catching yellowfin tuna (YFT) in the equatorial
Atlantic Ocean. In 2003 – 2005, especially in 2005, the monthly catch by deep LL was double those of other years. The spatial distribution of the nominal catch per unit effort (cpue) by deep LL showed the maximum aggregation of YFT in waters with temperature
above 24 – 258C. The YFT mainly aggregated in the equatorial Atlantic, extending east in the first and second quarters of the year. In
the third quarter of the year, the SST decreased off West Africa and the YFT migrated westwards to 158W. Results of PCA indicated
that higher subsurface water temperatures resulted in a deeper thermocline and caused a higher cpue of YFT, but the influence of
NTA on the cpue of YFT seemed to be insignificant.
Keywords: equatorial Atlantic Ocean, principal component analysis, satellite oceanography, yellowfin tuna.
Introduction
Analyses of tagging and catch-at-size data have demonstrated that
yellowfin tuna (YFT; Thunnus albacares) undertake migrations of
different scales in the tropical Atlantic Ocean (Anon., 2004). YFT
are fished between 458N and 408S by a commercial tuna longline
(LL) fishery and surface gear (purse-seine, live bait, and handline)
in the tropical eastern Atlantic Ocean (Anon., 2004). The LL
fishery began at the end of the 1950s and soon gained popularity
with significant catches in the early 1960s. The popularity of targeting YFT spread over the entire Atlantic Ocean (Anon., 2004).
In the early 1990s, the Taiwanese fishing fleet began to switch
their target species from albacore and YFT to bigeye tuna, using
deep LL gear. There were two types of LL, regular LL and deep
LL, defined by the number of hooks between two floats (NHB;
Lee et al., 2005a). If the LL forms a theoretical catenary curve,
placement of hooks on the LL can represent the depth where the
hooks are deployed, and hence the depth where the fish are
found (Lee et al., 2005a). To conduct more realistic or unbiased
analyses of the tuna resource, it is necessary to separate these
two types of LL and treat them as different gears, because they
target different species. Accordingly, the Taiwanese government
has collected NHB information from logbooks of fishing vessels
in the three major oceans since 1995. YFT was then still a
bycatch with deep LL gear, but its catch had increased significantly
# 2011
since the 1990s and comprised 40% of the total YFT catch from
LL (Hsu et al., 2001). In contrast to the increasing catches of YFT
in other oceans, there has been a steady decline in overall Atlantic
catches, with an overall decline of 45% since the peak catches of
1990 (ICCAT, 2009). The catches from surface fisheries in the
Atlantic Ocean decreased between 2001 and 2004, whereas LL
catches increased (ICCAT, 2006). After 2006, the catches of the
LL fishery declined, although some surface fisheries increased in
the eastern Atlantic (ICCAT, 2009).
The currently accepted stock structure of YFT in the Atlantic
Ocean (Bard and Hervé, 1994; Fonteneau and Soubrier, 1996)
suggests that juveniles move along the west African coast from
the main spawning area in the Gulf of Guinea. A number of
factors can affect tuna distribution, such as temperature, salinity,
and forage (prey), and links between environment and populations of large pelagics have been described for temperate
waters. Previous studies indicated that the YFT prefer warm
water and that their distribution is affected by sea surface temperature (SST; Stretta, 1991; Kumari et al., 1993; Lee et al., 1999; Mohri
and Nishida, 2000), subsurface temperature (Bautista-Cortes,
1997), and the depth of the thermocline (Robinson et al., 1976;
Suzuki et al., 1978). Tuna also occur in regions with high values
of chlorophyll a (Chl a) and net primary production (NPP;
Lehodey et al., 1998; Lee et al., 1999). It is speculated that the
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1064
K-W. Lan et al.
Table 1. Climate and oceanographic factors collected from satellite data and model assimilation in this study.
Category
Climatic index
Oceanography
Variables
NTA
SST
ST 55– 329
Chl a
NPP
WS
SSHD
Annotation
North tropical Atlantic index
Sea surface temperature
Sea temperature at 55– 329 m depth
Chlorophyll a
Net primary production
Windspeed
Sea surface height deviation
high Chl a areas and SST fronts influence local ecosystems, as well
as commercial fisheries (Lan et al., 2009). The biophysical environment plays an important role in controlling the distribution and
abundance of tuna (Lee et al., 1999). Large-scale environmental
and climatological variability also affects the distribution and
production of tuna populations (Anon., 1989; ICCAT, 1997).
Strong anomalies, such as the 1984 El Niño in the Pacific, have
been reported as the cause of a sudden decrease in catches of
YFT in the purse-seine fishery (ICCAT, 1998). Santiago (1997)
also found that the Northern Atlantic Oscillation (NAO) was
strongly negatively correlated with the recruitment of northern
albacore and weakly positively correlated with that of eastern
bluefin tuna. A deeper thermocline and increased SST might
have affected yellowfin recruitment and purse-seine catchability.
In the tropical Atlantic, the effects of environmental anomalies
on YFT are less obvious, but not absent. The availability of oceanographic and biological information would improve fishing operations and the management of fisheries resources. Seasonal
variations in hydrographic conditions can be observed broadly
and immediately using satellite remote sensing (Lee et al.,
2005b). Understanding the effects of environmental conditions
on fish populations is an essential step towards ecosystem-based
management of fisheries, which is increasingly becoming a standard approach in policy development (Pikitch et al., 2004). Joint
analysis of time-series of satellite and in situ catch data can be
used to identify changes in pelagic fish habitats and their
impacts on migration, size, or recruitment of a particular fish
stock and help to regulate maximum catches and conserve fisheries
resources (Zagaglia et al., 2004).
In this study, we used the north tropical Atlantic index (NTA)
as our climate index and several environmental variables, including satellite-derived SST, Chl a, NPP, windspeed (WS), sea
surface height deviation (SSHD), and model-simulated subsurface
temperatures to examine the impact of these factors in the equatorial Atlantic on YFT catch per unit effort (cpue), according to
fishing data reported by the Taiwanese LL fishing fleet. Fishing
of YFT is conducted throughout the tropical Atlantic Ocean,
especially in equatorial waters. We chose the region between
208N and 108S and 608W and 158E as our study area. The purposes
of this study are to investigate: (i) the spatiotemporal fishing conditions of YFT catch by two types of LL (regular LL and deep LL),
and (ii) the distribution and variations in cpue associated with
climatic and oceanographic factors.
Material and methods
Environmental data
In this study, we used NTA as the climatic index factor and six
kinds of satellite-derived and model-simulated data as oceanographic factors, including (i) Chl a, (ii) NPP, (iii) SST, (iv) subsurface temperatures at depths of 55 –329 m, (v) WS, and (vi) SSHD.
Data source
NOAA ESRL
NOAA AVHRR
ECCO Kalman Filter
OceanColor Web (SeaWiFS)
Ocean Productivity Web
TRMM Microwave Imager
AVISO programme
The environmental factors used in this study are summarized in
Table 1.
Chl a concentration
We used Global Area Coverage monthly composite SeaWiFS
images with a 9-km spatial resolution downloaded from the
NASA Ocean Color Web (http://oceancolor.gsfc.nasa.gov/). The
SeaWiFS dataset started in September 1997; however, the series
chosen for our analysis was from January 1998 to December
2007. The ocean colours of SeaWiFS Level 3 images were used
to estimate the sea surface Chl a.
Net primary production
We used the vertically generalized production model (VGPM)
developed by Behrenfeld and Falkowski (1997) to estimate
monthly NPP during the period January 1998 to December
2007. The VGPM estimates NPP using a temperature-dependent
description of chlorophyll-specific photosynthetic efficiency,
where NPP is a function of chlorophyll, available light, and photosynthetic efficiency.
SST and subsurface temperature
Satellite-derived SST data were collected with the Advanced Very
High Resolution Radiometer (AVHRR) sensor on board the
National Oceanic and Atmospheric Administration (NOAA) satellites and downloaded from the National Environmental Satellite,
Data, and Information Service (NESDIS). The AVHRR SST data
with a spatial resolution of 4 km were processed using the
Multichannel Sea Surface Temperature (MCSST) algorithm
version 4 (Li et al., 2001). In addition, subsurface temperatures
were produced by the Estimating the Circulation and Climate of
the Ocean (ECCO) Kalman Filter–routine global ocean data
assimilation system; the system is based on the parallel version
of the Massachusetts Institute of Technology general circulation
model and an approximate Kalman filter method (Fukumori
et al., 2000). The model has a telescoping latitudinal grid with
1/38 resolution in the tropics (208S–208N), and the resolution
in longitude is 18. The model is forced by National Center for
Environmental Prediction (NCEP) reanalysis products
(12-hourly windstress, daily heat, and freshwater fluxes) with
time-means replaced by those of the Comprehensive
Ocean-Atmosphere Dataset. According to Bard et al. (1999), the
YFT can attain a depth of 350 m; therefore, in this study, we
used temperature at six levels (55, 105, 155, 208, 279, and
329 m) as our subsurface temperature factors.
Surface winds
The surface wind measurements were derived from the SeaWinds
instrument aboard NASA’s QuikSCAT satellite. The SeaWinds
1065
Effect of ocean conditions on fishing for yellowfin tuna
scatterometer bounces radar signals off the ocean surface and
measures the strength of the returning backscatter signal. The
combination of these measurements was processed to wind velocity (speed and direction) using NASA-developed wind-retrieval
algorithms (Freilich, 2000; Lungu, 2001). All computations were
performed on the individual swathe data, which were then
mapped to an equal-angle grid of 0.258 latitude by 0.258 longitude,
using a simple arithmetic mean to produce individual and composite images of various durations.
monthly. The Chl a, NPP, SST, and subsurface temperature data
were then also calculated as monthly means on a spatial grid of
5 × 58 from January 1998 to December 2007. The relationship
of fishery cpue and environmental factors was estimated using
the PCA. We normalized all data using the formula described by
Johnson and Wichern (1988) for PCAs. Both monthly fishery
cpue and the environmental variables were used as independent
variables. Data for each variable were standardized by a linear
transformation using the formula
Xi − X
,
s.d.
Sea surface height deviation
We received SSHD from the AVISO (Archiving, Validation and
Interpretation of Satellite Oceanographic data) programme.
Mean altimetry profiles were calculated for different satellite
missions by reviewing historical altimetry data. Raw data were
received and processed to altimetry data for each individual
satellite. They were then processed to SSHD by removing the
mean SSH and merged (Ducet et al., 2000). Data were mapped
to an equal-angle grid of 0.258 latitude by 0.258 longitude.
North tropical Atlantic SST index
The NTA values were downloaded from the Earth System Research
Laboratory (http://www.esrl.noaa.gov/psd/data/climateindices/
list/#NAO). Penland and Matrosova (1998) applied empirical
orthogonal functions analysis for the tropical Atlantic SST
anomalies (region 1008W–208E 308S–308N) to predict NTA
using 20 main empirical orthogonal functions. Anomalies were
calculated relative to the 1951– 2000 climatology; month of data
is the centre of the three months that are smoothed.
YFT fisheries data
The catch and effort data were compiled from logbooks of
Taiwanese LL fishing vessels in the equatorial Atlantic waters
(208N–108S 608W– 158E) provided by the Overseas Fisheries
Development Council (OFDC) of Taiwan. These data were
collected from January 1998 to December 2007 to match the
SeaWiFS Chl a data. They included the number of hooks,
fishing time, area, and catch of the YFT; geographically, monthly
means were plotted on a 5 × 58 spatial grid. Regular and deep
LLs datasets were used in this study. Approximately 75% of the
Taiwanese LL data for 1998–2007 included NHB information
for the Atlantic Ocean, which was used as the learning dataset.
Data analysis
The YFT can reach a depth of 350 m; however, they spend most of
their life in the surface layer above 100 m (Bard et al., 1999; Brill
et al., 1999). The Taiwanese LL fishery primarily targets YFT at
an operational depth of 50 –120 m for regular LL and 50 – 200 m
for deep LL (Lee et al, 2005a), where YFT are abundant
(Blackburn, 1965; Brill et al., 2005). We collected temperature profiles from the sea surface to 329 m, aggregated into 1 m intervals.
We selected temperature strata for our analysis by eliminating
depths with high correlations (.0.8) with other depths (Tsai
and Chai, 1992). Inclusion of all depth strata in the principal component analysis (PCA) resulted in an overestimate of the real contribution of each eigenvector. The selected temperature strata were
considered the variables that were the most appropriate proxies of
a suite of processes.
The cpue was used as a relative abundance index of YFT. It was
calculated as the number of individuals caught per 1000 hooks
(103 fish hook21) on a 5 × 58 grid, and values were averaged
(1)
where Xi refers to the data for month i, and X and s.d. are the relative mean and standard deviation of the data, respectively. The
factor analysis model of the PCA was used to describe associations
of YFT with environmental variations. Eigenvalues and the proportions of dependent and independent variables were selected
according to the factor loading and cumulative proportions of
the variables. The principal component scores were estimated by
the following formula:
Zi =
n
Eij × Sij ,
(2)
j=1
where Zi is the score of the principal component i, Eij the eigenvalue of variable j in principal component i, and Sij the
standardized value of variable j in principal component i. A principal component with a large proportional value suggests that it
has a better distribution and, therefore, better relationships
among elements.
A geographic information system was used to construct a database fishery and environmental datasets. All statistical analyses
were performed using StatSoft Statistica 6.0.
Results and discussion
Temporal variations and spatial distribution of cpue
Using the learning dataset, the definition of regular LL and deep
LL was investigated by analysing the NHB information. The
range of NHB in this dataset was 6 –20 (Figure 1), and it had
a bimodal distribution. A smaller number of NHB denoted
regular or shallow LL, whereas the bigger number indicated deep
LL. Based on the patterns of Figure 1 and the fishing habits of
Taiwanese LL fishing vessels, this study defined regular LL as
6 ≤ NHB ≤ 12 and deep LL as 13 ≤ NHB ≤ 20.
Figure 2a shows the time-series of YFT caught (1998–2007) by
regular LL and deep LL. The catch of YFT by regular LL was lower,
whereas most of the YFT were caught by deep LL in the equatorial
Atlantic Ocean; the increasing time-series trend of the catch by
deep LL was higher. From 1998 to 2005, monthly catches of YFT
by regular LL were always less than 30 t and almost zero after
2006. However, the mean catch of deep LL was 86.9 t, with
higher values recorded in 2003–2005, when the monthly catch
(average catch of 194.58 t) by deep LL was double that of other
years, especially in 2005, the catch of YFT having increased from
247.9 t in January to 516.4 t in April. Figure 2a reveals that the
deep LL was the main fishery in the equatorial Atlantic Ocean;
Figure 2b shows the time-series nominal cpue of YFT by deep
LL from 1998 to 2007. The mean nominal cpue of deep LL was
0.94 × 103 fish hook21 and the values were higher from 2003 to
1066
Figure 1. Frequency distribution of the NHB in the learning dataset
from 1998 to 2007 (n ¼ 128 187).
Figure 2. Annual trends in the Taiwanese LL fishery (a) catch of YFT
with regular LLs (grey line) and deep LLs (black line), and (b) cpue for
YFT with deep LLs from 1998 to 2007.
2005, especially in 2005 when the nominal cpue was 1.09 –
3.05 × 103 fish hook21.
Figure 3 shows the spatial distribution of nominal cpue by deep
LL overlaid on SST images for four quarters of the year (mean
value for 1998– 2007). The map reveals that the YFT aggregated
mostly in waters with temperature above 24 –258C. The YFT in
the equatorial Atlantic extended from west to east in the first
two quarters of the year. However, they moved to west of 158W
on the equator in the third quarter and gradually returned to
the eastern part of their distribution in the fourth quarter.
Relationships between cpue and environmental variables
In Table 2, the correlation analysis demonstrated that there are
high correlations between subsurface temperatures from 105 to
329 m; we only chose three of these variables (ST_105, ST_208,
K-W. Lan et al.
and ST_329) for the PCA. Similarly, there was a strong autocorrelation between Chl a and NPP, and we selected NPP as the productivity factor. When all correlated variables were used in the
analysis, the real contribution of each eigenvector was overestimated artificially. Ten environmental factors, including deep LL
fisheries data and oceanic factors, were chosen for the PCA.
The percentage variance and eigenvalue of each principal component are given in Table 3. Three principal components had
eigenvalues .1. We decided to use the corresponding first
(41.62%), second (18.87%), and third eigenvalues (11.34%),
which accounted for 71.82% of the cumulative variance.
Factor loadings of the first three components of the PCA of ten
variables are listed in Table 4. Factor loadings with highly positive
correlations for the first component were cpue (20.63), SST
(20.65), ST_55 (20.79), ST_105 (20.75), ST_208 (20.85),
and NTA (20.77) and high negative correlations in NPP (0.56).
The second component revealed high positive correlations in
SST (20.61) and WS (20.73) and high negative correlations in
NPP (0.59). Scatterplots of the first and second component axes
in Figure 4 and its corresponding time-varying amplitude in
Figure 5 illustrate the distribution pattern of the environmental
factors associated with fishery factors and monthly variations,
respectively. In Figure 4, the associations indicate that cpue of
deep LL increased with higher subsurface temperatures (ST_105
and ST_205) and NTA; the time-varying amplitude in Figure 5a
revealed that the negative amplitude variation was stronger from
2003 to 2006. Figure 5b shows that the SST, WS, and NPP had a
seasonal variation in the second component, with variation of
SST and WS being opposite to that of NPP.
It is necessary to separate the regular and deep LL, because they
targeted different species and used different fishing gears
(Lee et al., 2005a). After the daily LL had been separated into
two types of LL, the results indicated that the deep LL was the
major fishery catching YFT in the equatorial Atlantic Ocean and
the regular LL had a very low catch (Figure 2a). The effort
aimed at deep LL also increased more than that aimed at regular
LL in recent years. Lee et al. (2005a) indicated that the higher
nominal YFT cpue by deep LLs resulted from that gear usually
operating in equatorial areas where YFT are more abundant,
whereas the regular LL operated in high latitudes and primarily
targeted albacore (Thunnus alalunga) and swordfish (Xiphias
gladius). Stretta (1991) indicated that in the tropics, most YFT
preferred warm water with a narrow temperature range of
22 –298C, particularly above 258C. Similar results were obtained
by Kumari et al. (1993) and Lee et al. (1999), who demonstrated
that YFT are distributed in regions with SST.258C. The current
study also confirmed that the YFT aggregate mostly in warm
water with temperature above 24 –258C in the equatorial
Atlantic (Figure 3).
Figure 3 shows that in the equatorial Atlantic, YFT migrated
eastwards during the first half of the year, and moved to west of
158W in the third quarter and gradually returned to the eastern
part of their distribution in the fourth quarter. YFT prefer warm
water, particularly above 258C, but the SST decreases off West
Africa in the third quarter to below 228C. The SST might
become more influential in the presence of strong surface
thermal gradients (fronts), whereas the distributions of YFT
usually cover a broader range in other areas (Laurs et al., 1984;
Zagaglia et al., 2004). Maury et al. (2001) also pointed out that
such seasonal migration should be related to the annual cycle of
the meridional oscillation of warm water. The annual reproductive
1067
Effect of ocean conditions on fishing for yellowfin tuna
Figure 3. The spatial distribution of cpue for YFT using deep LLs overlaid on SST images for the four quarters (mean value for 1998– 2007
Taiwanese LL fisheries data by 58 rectangle).
Table 2. The correlation matrices analysis of 14 factors selected in this study to eliminate spurious high correlation factors (.0.8).
Parameter considered
Cpue
SST
ST_55
ST_105
ST_155
ST_208
ST_279
ST_329
Chl a
NPP
WS
SSHD
NTA
Cpue
1.00
0.14
0.38
0.45
0.52
0.55
0.52
0.21
20.13
20.11
0.05
0.25
0.49
SST
0.14
1.00
0.50
0.22
0.26
0.41
0.59
0.19
20.49
20.74
0.16
0.23
0.54
ST_55
0.38
0.50
1.00
0.44
0.53
0.69
0.73
0.20
20.41
20.52
0.52
0.23
0.54
ST_105
0.45
0.22
0.44
1.00
0.84
0.76
0.77
0.76
20.25
20.13
20.07
0.31
0.48
ST_155
0.52
0.26
0.53
0.84
1.00
0.94
0.79
0.40
–0.19
–0.17
0.05
0.29
0.48
transatlantic displacements of the adult population are probably
driven by temperature to warmer locations favourable for juveniles. Figure 3 indicates high catch and high cpue period in the
second quarter, and the monthly mean WS maps (Figure 6)
display the Intertropical Convergence Zone (ITCZ) at the
southern position in the second quarter, and stronger WS in the
west than in the east Atlantic. In the third quarter, the ITCZ is displaced to its northern position by stronger southeasterly trade
winds and extends to the west equatorial Atlantic and causes the
SST in the eastern area to decrease to a level unfavourable for
fishing. As the trade winds along the equator intensify, the resulting zonal pressure gradient in the ocean and associated uplifted
thermocline result in seasonal cooling of SST in the eastern equatorial Atlantic (Xie and Carton, 2004). Figure 7 shows the SST and
NPP monthly mean variance in the east (108S–208N 158E – 158W;
Figure 7a and c) and west (108S– 208N 158W–608W; Figure 7b
and d) equatorial Atlantic Ocean divided according to the
ICCAT geographic delimitations. The SST was always higher in
the west than in the east, especially after July, the uplifted thermocline resulting in cooling of the SST to below 268C and higher NPP
(.700 mg C m22 d21) in the east. Therefore, the turbulent
marine conditions in the eastern equatorial waters reduce the
size of the preferred area, resulting in concentration of the YFT.
High WS has been reported to affect fish vulnerability to capture
ST_208
0.55
0.41
0.69
0.76
0.94
1.00
0.90
0.33
20.27
20.32
0.15
0.33
0.55
ST_279
0.52
0.59
0.73
0.77
0.79
0.90
1.00
0.51
20.45
20.50
0.13
0.37
0.63
ST_329
0.21
0.19
0.20
0.76
0.40
0.33
0.51
1.00
20.24
20.05
20.28
0.24
0.42
Chl a
20.13
20.49
20.41
20.25
20.19
20.27
20.45
20.24
1.00
0.84
20.20
20.35
20.23
NPP
20.11
20.74
20.52
20.13
20.17
20.32
20.50
20.05
0.84
1.00
20.34
20.32
20.26
WS
0.05
0.16
0.52
20.07
0.05
0.15
0.13
20.28
20.20
20.34
1.00
20.10
0.01
SSH
0.25
0.23
0.23
0.31
0.29
0.33
0.37
0.24
20.35
20.32
20.10
1.00
0.34
NTA
0.49
0.54
0.54
0.48
0.48
0.55
0.63
0.42
–0.23
–0.26
0.01
0.34
1.00
by modifying fish behaviour and availability to a specific depth
of the fishing gear (Bigelow et al., 1999). The same phenomenon
was also noted by Carey and Robinson (1981), who established
that swordfish are deeper during strong winds, so reducing their
vulnerability to capture.
In the PCA results, the first principal component revealed that
SST was not the only major environmental factor and that it
showed lower positive correlation with cpue than ST_105,
ST_208, and NTA. It should also be noted that species of the
genus Thunnus possess a blood circulatory system that regulates
their body temperature efficiently and allows them to make large
horizontal and, particularly, vertical excursions (Zagaglia et al.,
2004), which are likely to reduce the correlation between tuna
catch and the SST field. This suggests that the cpue was positively
linearly related with variations in subsurface water temperature.
In other words, higher subsurface water temperatures, particularly
between 105 and 208 m (Figure 4), resulted in a deeper thermocline and a higher cpue for YFT. Maury et al. (2001) also indicated
that the relationship of catches with temperature at 150 m depth is
statistically more significant. The Taiwanese LL fishing vessels
using deep LL have an operating depth of 50 –200 m (Lee et al.,
2005a), where temperatures generally exceed 208C and YFT are
abundant. Tunas prefer warm water, where they position themselves after deep diving in cooler water (Holland et al., 1992;
1068
K-W. Lan et al.
Table 3. Eigenvalues, variance, and cumulative variance for the
PCA in terms of YFT cpue by deep LL.
Principal
component
1
2
3
4
5
6
7
8
9
10
Eigenvalue
4.16
1.89
1.13
0.85
0.70
0.47
0.40
0.21
0.14
0.05
Variance
(%)
41.62
18.87
11.34
8.47
6.99
4.74
4.00
2.09
1.36
0.52
Cumulative variance
(%)
41.62
60.48
71.82
80.30
87.29
92.03
96.03
98.12
99.48
100.00
Table 4. Results of the PCA (PC1 – PC3) for environmental factors
in terms of YFT cpue by deep LL.
Variable
Cpue
SST
ST_55
ST_105
ST_208
ST_329
NPP
WS
SSHD
NTA
PC1
20.63
20.65
20.79
20.75
20.85
20.53
0.56
20.19
20.49
20.77
PC2
0.20
20.61
20.37
0.49
0.08
0.41
0.59
20.73
0.13
0.11
PC3
20.40
0.46
20.27
20.15
20.27
0.18
20.41
20.47
0.42
0.06
Fonteneau, 1996). The second component displaying high positive
correlations in SST, WS, and high negative correlations in NPP
(Figure 4) had seasonal variation (Figure 5b). The seasonal variation was caused mainly by the changing trade winds. In the
third quarter, the stronger southeasterly trade winds caused the
SST in the eastern area to decrease (Xie and Carton, 2004),
especially after July, the rising thermocline resulting in cooling
of the SST and higher NPP in the east.
The PCA results also suggest that NTA is positively correlated
with cpue. The large-scale environmental and climatological variabilities affect the distribution and production of tuna populations
(Anon., 1989; ICCAT, 1997). Santiago (1997) also found that the
NAO was strongly negatively correlated with the recruitment of
northern albacore and weakly positively correlated with that of
eastern bluefin tuna. The negative NTA (positive NAO) indices
coincide with the strengthening of the easterly equatorial trade
winds (Ruiz-Barradas et al., 2000) and with lower SST in the equatorial eastern Atlantic. Figure 8a shows the time-series of annual
trends of YFT cpue by deep LL and NTA, and Figure 8b shows
the temperature anomaly at 105 m (STA_105) during the period
1998–2007. The cpue was higher in 2003–2006, especially from
January to April in 2005, and the highest value for NTA occurred
in the first half of 2005. In 1998 and at the end of 2001, NTA was
high, but cpue was low. NTA was higher in 1998 and 2001, but
STA_105 did not increase during the same period. The STA_105
increased from 2003 to 2006 and the cpue increased simultaneously; they were both highest during these years and the
cpue also displayed the highest value in 2005. Hence, higher subsurface water temperatures result in a deeper thermocline and a
higher cpue of YFT from 2003 to 2006, which confirms our
Figure 4. Ordination diagram of the PCA illustrating the indicator
cpue for YFT and effort using deep LLs in relation to environmental
factors of the first and second component.
Figure 5. The principle component modes: (a) first component and
(b) second component in the equatorial Atlantic Ocean during the
period 1998– 2007.
earlier statement. The NTA did not seem to be a major factor
affecting the catch of YFT. Die et al. (2002) discussed the NAO
and the catch of YFT and indicated that there is no clear evidence
that the NAO can be used to study the unexplained variation in
YFT biomass or catchability. Our study also demonstrated that
there is considerable uncertainty about the status of the relationship between YFT abundance and NTA, but it is unrealistic to
draw any conclusion from what might have happened during a
single year or a short period. To examine the relationship
between YFT stocks and climatic index, one needs time-series
data for a long period.
The influence of Chl a and NPP on the cpue of YFT results from a
lag effect on trophic conversion. Ortega-Garcı́a and Lluch-Cota
(1996) found a 3- to 5-month time-lag between high pigment concentrations and high tuna abundance in the tropical Pacific.
Lehodey et al. (1998) also noted 3- to 7-month time-lags between
phytoplankton peaks and the highest concentration of tuna prey.
Areas with steep bathymetry, such as continental slopes, seamounts,
and sea canyons, often produce upwelling, which forces deep water
rich in nutrients to the surface, thereby forming good tuna fishing
grounds (Nishida et al., 2005). In the equatorial east Atlantic, the
North African monsoon, as well as the tilting of the equatorial
1069
Effect of ocean conditions on fishing for yellowfin tuna
Figure 6. NASA’s QuikSCAT monthly climatology WS (m s21) for the four quarters. The black ellipses indicate ITCZ position.
Figure 7. Temporal variability in the east and west equatorial Atlantic Ocean: (a and b) SST and (c and d) net primary production (mean
value for 1998– 2007).
thermocline, induce upwelling of cool nutrient-rich water along the
coast (Crawford et al., 1990; Boyd et al., 1992). Ménard et al. (2000)
examined the stomachs of adult YFT caught from free schools and
found 83.3% of them to be non-empty, indicating that prey fish in
upwelling areas in the eastern Atlantic make up the largest component of YFT’s diet. Another area of high tuna abundance is off
South America, where coastal upwelling, because of the prevailing
winds, is responsible for high regional productivity
(De Anda-Montañez et al., 2004). The other reason for the high
catch and high cpue of YFT might have been the presence of
recruits. Juvenile YFT stay in the coastal areas of the equatorial
region, whereas pre-adults and adults move offshore. The average
weight of YFT caught in the Atlantic by the purse-seine fishery is
14 –8 kg, but the average weight caught by LL is 27 –51 kg
(Anon., 2004). Nishida et al. (2004) also indicated that the strong
recruitment of mature YFT probably caused large catches of LL.
(i) After the daily LL was separated into two types of LL (regular
LL and deep LL), the results indicated that the deep LL was
the main fishery catching YFT in the equatorial Atlantic
Ocean. In 2003–2005, the monthly catch by deep LL was
double that of other years, notably that of 2005.
(ii) The spatial distribution of the nominal cpue of deep LL
exhibited greatest aggregation of YFT in waters with temperature above 24– 258C. YFT mainly occupied the equatorial
zone, extending east in the first half of the year. After July,
lower SST caused by stronger wind off West Africa caused
the YFT to move west of 158W in the third quarter of the
year.
(iii) The PCA results indicated that higher subsurface water temperatures resulted in a deeper thermocline and a higher cpue
for YFT, whereas the NTA seemed not to be the major
index to affect the catch of YFT.
Conclusions and future research
In this fishery oceanography study, we investigated the distribution
of YFT populations in the equatorial Atlantic associated with
environmental factors. We reached the following conclusions.
Apparently, adequate biological and oceanographic information
would help fishery operations and would result in better management of the natural resource. Our analysis was inadequate to
1070
K-W. Lan et al.
University of Rhode Island kindly helped improve the language
used in the manuscript.
References
Figure 8. Annual trends in (a) cpue for YFT using deep LLs (black
line), NTA (grey dash line) and (b) sea temperature anomaly at
105 m (STA_105, black dash line) during the years 1998 –2007.
speculate about how particular changes in the subsurface temperature are related to the environment and about the influence of NTA
on the stock of YFT. Rather, we identified several issues for future
research. First, collect more long-term LL data from different
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analysis, such as wavelet analysis. In this study, we used 5 × 58
spatial grid fisheries data, and the environmental data were calculated as monthly means on a spatial grid of 5 × 58. Using the 58
grid, averaged environmental data might result in a loss of information; a finer resolution at a 18 grid is important for fisheriesenvironment studies and will be adapted in future research.
Second, examine the prey of YFT, such as cephalopods and small
pelagic fish, and the relationship between their abundance and
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should be used in future. A better understanding of the relationships between oceanic environments and fishing conditions
could make utilization of YFT more efficient, profitable, and
sustainable.
Acknowledgements
This study was supported financially by the Council of Agriculture
[98AS –10.1.1 –FA–F6(2) and 99AS –10.1.1 –FA–F6(3)] and the
National Science Council (NSC98 –2611–M –019 –008). We are
grateful to the Overseas Fisheries Development Council (OFDC)
of Taiwan for providing data from the Taiwanese longline
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