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Fishery Bulletin 111(2) 
thermocline and come to the surface periodically (Pep- 
pered, 2010). The main depth range of fishing for Big- 
eye Tuna in the Indian Ocean is 161—280 m (Mohri and 
Nishida, 1999), although they can inhabit the depth 
range of 0-100 m during the night (Howell et al., 2010). 
Sea-surface-height can be used to infer oceanic fea- 
tures such as current dynamics, fronts, eddies, and con- 
vergences (Polovina and Howell, 2005), and sea-surface 
temperature (SST) has been used to investigate pro- 
ductive frontal zones (Zainnudin et ah, 2004), both of 
which can be used to indicate potential tuna fishing 
grounds. Thermal (or color) gradients in satellite im- 
ages that arise from the circulation of water masses 
often indicate areas of high productivity (Saitoh et al., 
2009). Chlorophyll-a data can also be used as a valu- 
able indicator of water mass boundaries and may iden- 
tify upwelling that can influence tuna distribution in 
a region. 
The effects of ENSO on oceanographic conditions and 
tuna catches in the Pacific have been reported widely 
(Lehodey et al., 1997; Torres-Orozco et al., 2006; Briand 
et al., 2011). High catch rates of Albacore ( Thunnus 
alalunga ) in the southwest Pacific Ocean were found 
to correspond with high negative Southern Oscillation 
Index values during strong El Nino events (Briand et 
al., 2011). The effects of ENSO events on Bigeye Tuna 
catches have been well studied in the western Pacific 
Ocean (Miller, 2007) but less studied in the Indian 
Ocean. Most Indian Ocean studies have focused on the 
relationship between oceanographic parameters and 
the distribution of Bigeye Tuna (Mohri and Nishida, 
1999; Song et al., 2009, Song and Zhou, 2010), the cor- 
relation of a single oceanographic factor with ENSO 
(Yoder and Kennely, 2003), or oceanographic variabil- 
ity in the interior Indonesian seas (Zhou et al., 2008; 
Sprintall et al., 2009). 
Here, we focus on the ways in which climate vari- 
ability affects oceanographic conditions 
and catch rates of Bigeye Tuna in the 
EIO off Java. To obtain a more detailed 
description of the spatiotemporal charac- 
teristics of those oceanographic param- 
eters, we applied the empirical orthogo- 
nal function (EOF). Further analysis was 
undertaken with a generalized additive 
model (GAM) to examine the relationship 
between oceanographic conditions and 
catch rates of Bigeye Tuna. The ultimate 
goal of this study was to understand how 
catch rates of Bigeye Tuna in the EIO off 
Java are affected by ENSO events. 
90°E 1 00°E 1 1 0°E 1 20°E 130°E 14Q°E 150°E 
0 220 440 km 
Figure 1 
(A) Map of the Indonesian seas, with the inset box representing the study 
area. (B) Map of the study area in the eastern Indian Ocean (EIO) off Java 
for our analyses of how E! Nino-Southern Oscillation events may affect catch 
rates of Bigeye Tuna ( Thunnus obesus). In panel B, the wave and current 
systems in the EIO off Java are indicated by the dotted line for the South 
Java Current (SJC), solid lines for the Indonesian Throughflow (ITF), the line 
with dashes and 2 dots for the Indian Ocean Kelvin Waves (IOKWs), the line 
with dashes and 1 dot for the Rossby Waves (RWs), and the dashed line for 
the Indian Ocean South Equatorial Current (SEC). 
Materials and methods 
Study area 
The study area was located in the 
EIO, south of Java, spanning be- 
tween 6-16°S and 104— 126°E (Fig. IB). 
This area has complex dynamic currents 
and wave systems. The dominant current 
and wave features include 1) the Indo- 
nesian Throughflow (ITF), outflow wa- 
ter from the Pacific to the Indian Ocean 
(Molcard et al., 2001; Gordon et al., 
2010); 2) the seasonally reversing South 
Java Current (SJC) along the southern 
coast of the Indonesian Sea (Sprintall 
et al., 2010); 3) the Indian Ocean South 
Equatorial Current (SEC) that flows from 
the southern Indian Ocean to an area 
off southern Java (Zhou et al., 2008); 4) 
downwelling Indian Ocean Kelvin Waves 
(IOKWs) that propagate to the east along 
the coasts of west Sumatra, Java, and the 
lesser Sunda islands (Syamsudin et al., 
