184 
Fishery Bulletin 111(2) 
-20 -10 0 10 20 
24 25 26 27 28 29 
SST 
0.00 0.10 0.20 0.30 
Chi-a 
Figure 6 
Effect of the 3 oceanographic variables — (A) sea- 
surface-height anomaly (SSHA), (B) sea-surface 
temperature (SST), and (C) chlorophyll-a (chl-a) 
concentrations — on Bigeye Tuna ( Thunnus obe- 
sus) catches derived from generalized additive 
model (GAM) of presence-absence data. The r-axis 
shows the values of the explanatory variables, and 
the y-axis shows the results of smoothing the fit- 
ted values. The tick marks on the horizontal axis 
represent the values of the observed data points; 
the solid line indicates the fitted function. Dashed 
lines represent 95% confidence intervals. The hori- 
zontal line at zero indicates no effect. The percent 
frequency of occurrence was higher for all values 
for which the fitted GAM function was above the 
zero axis and lower for values <0. 
results are consistent with the work of Murtugudde 
et al. (1999), who showed that, in the Indian Ocean, 
intense El Nino events, such as the one in 1997-98, 
have direct effects on primary production and cause 
anomalous high values of chlorophyll-a concentration ob- 
served in the EIO. Upwelling areas are potential conver- 
gence zones for plankton aggregation, attracting larger 
predators, such as tunas (Lehodey et ah, 1997). Such 
concentrations of chlorophyll-a may cause the increased 
catches during El Nino event (Polovina et ah, 2001; Le- 
hodey et ah, 2003; Polovina et ah, 2004; Miller, 2007). 
We used a binomial GAM to investigate the effects 
of environmental variables that affect the catchability 
of Bigeye Tuna. The effects of oceanographic conditions 
inferred from the GAM indicated that oceanographic fac- 
tors strongly influence the catchability of Bigeye Tuna. 
SST was a more important oceanographic predictor of 
Bigeye Tuna catches than were the other environmental 
variables (SSHA and chlorophyll-a) in this region. Fur- 
thermore, this result from GAM analyses of SST indicates 
that remote forcing from the Pacific Ocean has a large ef- 
fect on HR during an El Nino because of the reduction in 
heat transported from the Pacific to the Indian Ocean by 
the Indonesian Throughflow during El Nino events (Ffield 
et ah, 2000; Gordon et al., 2010). Bigeye Tuna are very 
sensitive to changes in SST (Holland et al., 1992; Brill et 
ah, 2005). Our results indicate that Bigeye Tuna catches 
increased in areas with relatively low temperatures (24- 
27.5°C) and decreased at temperatures >27.5°C (Fig. 6B). 
Our results are supported by previous research from the 
North Pacific Ocean, in which SST had the greatest effect 
on Bigeye Tuna at temperatures of 23-26. 5°C (Howell et 
al., 2010). 
SSHA was the second-most significant oceanographic 
predictor of Bigeye Tuna catch distribution in the EIO 
off Java. We used SSHA to understand oceanic variabil- 
ity, such as current dynamics, eddies, convergences, and 
divergences, which could be used as proxies for the poten- 
tial location of tuna catches (Polovina and Howell, 2005). 
Our study showed that the preferred habitat for Bigeye 
Tuna was in the range of SSHA values of -21 to 5 cm 
(Fig. 6A). This finding indicates that Bigeye Tuna forage 
in areas of low and negative SSHA values in contrast 
to divergences in SSHA values. Howell and Kobayashi 
(2006) also found the presence of a strong gradient of sea- 
surface height in the region of Palmyra Atoll during the 
1997-98 El Nino, coinciding with an increase in the geo- 
strophic (subsurface) flow that may increase shoaling of 
longline sets. Negative SSHA would push the thermocline 
upward, nearer the surface, and the elevation of the ther- 
mocline would allow Bigeye Tuna from below to become 
more accessible to longline gear. This preferred condition 
may enhance the potential Bigeye Tuna habitat, as it ap- 
parently did during the 1997-98 El Nino, when increased 
Bigeye Tuna catches occurred. Our findings seem to agree 
with the results of Holland et ai. (1992) and Brill (1994), 
who reported that Bigeye Tuna move toward cooler habi- 
tats to prevent overheating, with negative values of SSHA 
indicating that Bigeye Tuna are attracted only to shallow 
water when the thermocline is closer to the surface (Ar- 
rizabalaga et ah, 2008). 
Among the 3 environmental predictors assimilated 
in the model, chlorophyll-a concentrations exhibited the 
