Chen et at: A modeling approach to identify optimal habitat and suitable fishing grounds for Ommastrephes bartramii 
7 
A 
152°E 154°E 156°E 158°E 160°E 162°E 164°E 
B 
46°N 
44'N 
42°N 
40°N 
38°N 
/ZStk x 
m % - k 
152°E 154°E 1 56°E 158°E 160°E 162°E 164°E 
sss n< 
34.4 
34.0 
33.6 
33.2 
32.8 
32.4 
C 
152°E 1 54°E 156“E 158°E 160'E 162“E 164-E 
D 
152°E 1 54°E 1 56' E 158"E 160°E 162°E 164°E 
Figure 6 
The spatial distribution of fishing effort for Ommastrephes bartramii in the Northwest Pacific Ocean from the Chinese 
squid jigging fleets during August 2004 overlaid on (A) sea surface temperature (SST), (B) sea surface salinity (SSS), 
(C) sea surface height anomaly (SSHA), and (D) chlorophyll-a (chl-a) images. The numbers 19.0, 33.3, -5, and 0.3 in the 
maps represent 19.0 SST isotherm, 33.3 SSS isohaline, -5 cm SSHA and 0.3 mg/m 3 chi a, respectively. 
chl-a concentration of about 0.3 mg/m 3 (Fig. 6D) dur- 
ing August in 2004. 
Because these four environmental variables are 
closely related with O. bartramii distribution, we 
rescaled them with SI values (ranging from 0 to 1) 
based on histogram distributions (Figs. 3, 4, and 5). 
On the basis of the SI definition (Brown et al., 2000; 
Table 1), the highest fishing effort was given an SI 
value of 1, the total fishing effort below 2000 days 
was given an SI value of 0.1, and the total fishing 
effort between the highest fishing effort and 2000 
days was given an SI value of 0.5. The definitions of 
SI values for the four environmental variables are 
shown in Table 2. 
HSI model selection 
The different HSI models with one, two, three, and 
four environmental variables were evaluated for the 
most parsimonious HSI model. The HSI model with one 
variable (SST) was the best for predicting the percent- 
age of fishing effort in an area when the GMM was 
applied (Table 3), whereas the HSI model with three 
variables (SST, SSHA, and chi a) was the best when 
the AMM was applied (Table 3). When the same sets of 
environmental variables were used for the two empiri- 
cal HSI models, respectively, the AMM model yielded 
better results in predicting the fishing effort because 
its AIC value was less than that of the GMM model. 
