Syamsuddin et al.: Effects of El Nino- Southern Oscillation on catches of Thunnus obesus in the eastern Indian Ocean 
177 
2004); and 5) westward Rossby Waves propagation at 
12-15°S (Gordon, 2005; Sprintall et al., 2009). 
Besides these current and wave systems, winds over 
the Indonesian maritime continent and the position of 
the Intertropical Convergence Zone are dominant fea- 
tures of strong monsoon signatures. During the south- 
east monsoon (May to October), southeasterly winds 
from Australia generate upwelling along the southern 
coasts of Java and Bali. These conditions are reversed 
during the northwest monsoon (November to April) 
(Gordon, 2005). 
Data 
For our study, we used fishery catch data and satel- 
lite remotely sensed data. Bigeye Tuna catch data and 
remotely sensed environmental data for the period of 
1997-2000 were analyzed. These data included the 
ENSO components of an El Nino event (April 1997- 
May 1998) and a La Nina event (July 1998-June 2000). 
Fisheries data sets Catch data for Bigeye Tuna were 
obtained from longline fishing logbooks provided by PT 
Perikanan Nusantara, 2 an incorporated company of the 
Indonesian government, at Benoa, Bali. Data included 
fishing position (latitude and longitude), operational 
days, fish weight (in kilograms), vessel number, num- 
ber of hooks, and the number of fish caught per month 
during 1997-2000. The fishing locations recorded in the 
logbook were only the fishing positions where Bigeye 
Tuna were caught (there were no data for the locations 
where no fish were caught). These data were compiled 
into grids of 1° latitudexl 0 longitude because catch 
data for Bigeye Tuna were available only at a resolu- 
tion of 1°. From this data set, the catch rate of Bigeye 
Tuna was expressed as a percentage of hook rate (HR). 
The HR was calculated as the number of fish caught 
(individuals/month) per 100 hooks. The HR, therefore, 
shows how many tuna were hooked per unit of 100 
longline hooks, and the HR can be referred to as catch 
per unit of effort. 
The majority of fishing operations were conducted 
by medium-size vessels (100 gross tonnage). The num- 
ber of vessels in operation was 19-20 per month, and 
vessels used the same fishing gear (longline sets) and 
similar fishing techniques. The number of fishing sets 
was within the range of 910-1607 per year. The long- 
line sets were specifically designed and constructed to 
reach the swimming depths of the Bigeye Tuna. PT 
Perikanan Nusantara 3 used 2 types of longline sets 
constrained by the fishing depth during operation: 1) 
a shallow set (depth <100 m; consisting of 4-6 branch 
lines between floats) and 2) a deep set (depth 100-300 
2 Mention of trade names or commercial companies is for iden- 
tification purposes only and does not imply endorsement by 
the National Marine Fisheries Service, NOAA. 
3 Perikanan Nusantara, Inc. 2001. Vessels operation data of 
Incorporated Company of Perikanan Nusantara, 78 p. Peri- 
kanan Nusantara, Jakarta, Indonesia. 
m; consisting of 10-14 branch lines between floats). 
The deep set was used to catch Bigeye Tuna, and the 
shallow set was better suited to catchYellowfin Tuna 
( Thunnus albacares). The longline fishery targeted Big- 
eye Tuna at operational depths of 109—288 m during 
night sets. Each deep set consisted of 10-14 branch 
lines between floats and 800-1600 hooks per set. The 
fishing ground covered an area located around 10-16°S 
and 108-120°E; fishing operations were limited to 15 
days per trip because of fuel costs and the need to keep 
caught fish fresh (Perikanan Nusantara 3 ). 
Bigeye and Yellowfin Tunas were distinguished by 
various characteristics, including the following fea- 
tures: the Bigeye Tuna is longer, has a large head, 
large eyes, a dusky-colored tail, yellowish finlets edged 
in black, and a tail with a flat, trailing edge. The Yel- 
lowfin Tuna is shorter, has a smaller head, round and 
small eyes, a narrow body, a yellowish tail, and a notch 
in the center of its tail (Itano 4 ). 
Remotely sensed data Remotely derived environmen- 
tal variables included the sea-surface-height anomaly 
(SSHA), SST, and chlorophyll-a concentration. The 
SSHA data with a spatial resolution of 1/3°, derived 
from the TOPEX/Poseidon and ERS-1/2 altimeter mea- 
surements, were produced and distributed by Archiving, 
Validation and Interpretation of Satellite Oceano- 
graphic Data (AVISO, http://www.aviso.oceanobs.com). 
We obtained 7-day composite cycles of SSHA products 
to calculate the monthly mean SSHA. SST data were 
derived from the Advanced Very High Resolution Radi- 
ometer sensor on board NOAA satellites. This data set 
is distributed by the Physical Oceanography Distrib- 
uted Active Archive Center (http://podaac.jpl.nasa.gov) 
of the Jet Propulsion Laboratory of the National Aero- 
nautics and Space Administration (NASA). We used the 
monthly mean SST data set at a pixel resolution of 
4x4 km. Chlorophyll-a data were derived from images 
obtained from the 
Sea-viewing Wide Field-of-view Sensor (SeaWiFS) 
Project (level 3) and were of a spatial resolution of 9x9 
km for the period from September 1997 to December 
2000 (monthly composite data were downloaded from 
http://oceancolor.gsfc.nasa.gov). These data were pro- 
cessed with the SeaWiFS Data Analysis System vir- 
tual appliance (SeaDAS VA, vers. 6.1) of NASA (http:// 
seadas.gsfc.nasa.gov/seadasva.html). 
SSHA and SST images were matched with the 9-km- 
resolution spatial scale for chlorophyll-a concentra- 
tions. The 9-km-resolution data were used to capture 
dynamic features of the oceanographic conditions that 
represented El Nino and La Nina events and to show 
spatial patterns for the EOF analysis. However, for the 
4 Itano, D. G. 2005. A handbook for identification of yel- 
lowfin tuna and bigeye tuna in fresh condition, vers. 2, 27 
p. Pelagic Fisheries Research Program, Univ. Hawaii, Ho- 
nolulu. lAvailable from ftp://ftp.soest.hawaii.edu/PFRP/ 
itano/1 _ BE -YF%20ID%20Fresh_ ENGL ISH_v2 Iogo.pdf, ac- 
cessed November 2012.] 
