8 
Fishery Bulletin 108(1) 
Therefore, we determined that the AMM model with 
three variables, SST, SSHA, and chi a, was the most 
parsimonious HSI model (Table 3). 
To further compare the performances of AMM and 
GMM, we estimated the average actual percentage of 
fishing catch, average actual percentage of fishing ef- 
fort, and average CPUE of O. bartramii according to the 
grouped HSI values from AMM and GMM with three 
variables, SST, SSHA, and chi a, from August to Octo- 
ber, 1999-2004. From August to October, the area with 
the HSI value >0.6 had 58.8% of the total catch, 56.63% 
of the total fishing effort for the AMM model (Fig. 7, A 
and B), but 51.46% of the total catch and 46.56% of the 
total fishing effort based on the GMM model (Fig. 7, A 
and B). The area with the HSI value of less than 0.4 
yielded 15.48% and 38.58% of the total catch according 
Table 2 
Definition of suitability index (SI) values for the four environmental variables — sea surface temperature (SST), sea surface 
salinity (SSS), sea surface height anomaly (SSHA), and chlorophyll-a (chl-a) concentrations — used to develop a model to predict 
occurrences of Ommastrephes bartramii aggregations from August to October in the Northwest Pacific Ocean. 
Month 
SI 
SST (°C) 
Chi a (mg/m3) 
SSHA (cm) 
SSS (psu) 
August 
1 
>19 and <20 
>0.3 and <0.4 
>— 5 and <0 
>33.3 and <33.4 
0.5 
>17 and <19 
>0.2 and <0.3 
>-20 and <— 5 
>33.1 and <33.3 
>20 and <21 
>0.4 and <0.6 
>0 and <5 
>33.4 and <33.6 
0.1 
>15 and <17 
>0.1 and <0.2 
>-30 and <-20 
>33.0 and <33.1 
>21 and <25 
>0.6 and <0.9 
>5 and <15 
>33.6 and <34.0 
0 
<15 and >25 
<0.1 and >0.9 
<-30 and >15 
<33.0 and >34.0 
September 
1 
>16 and <17 
>0.4 and <0.5 
>—15 and <-10 
>33.3 and <33.4 
0.5 
>15 and 16 
>0.3 and <0.4 
>-25 and <-15 
>33.0 and <33.3 
>19 and <20 
>0.5 and <0.8 
>—10 and <0 
>33.4 and <33.5 
0.1 
>14 and <15 
>0.1 and <0.3 
>-30 and <-25 
>32.7 and <33.0 
>20 and <23 
>0.8 and <1.3 
>0 and <15 
>33.5 and <34.1 
0 
<14 and >23 
<0.1 and >1.3 
<-30 and >15 
<32.7 and >34.1 
October 
1 
>15 and <16 
>0.3 and <0.4 
>-15 and <-10 
>33.3 and <33.4 
0.5 
>13 and <15 
>0.4 and <0.7 
>—10 and <5 
>33.4 and <33.7 
>16 and <17 
0.1 
>10 and <13 
>0.2 and <0.3 
>-30 and <-15 
>32.9 and <33.3 
>17 and <21 
>0.7 and <1.3 
>5 and <20 
>33.7 and <34.1 
0 
<10 and >21 
<0.2 and >1.3 
<-30 and >20 
<32.9 and >34.1 
Table 3 
Parameters of linear regression model between the percentage of fishing effort and habitat suitability index (HSI) and AIC 
value for the two HSI models, geometric mean model (GMM) and arithmetic mean model (AMM), under different combinations 
of environmental variables, sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), 
and chlorophyll-a (chl-a) concentrations. The linear regression model can be written as Y=a+bX , where Y and X represent the 
percentage of fishing effort and HSI, respectively. hAIC is the difference in Akaike’s information criterion (AIC) values between 
that model and the best model. 
Environmental variables 
GMM 
AMM 
a 
b 
AIC 
4AIC 
a 
b 
AIC 
4AIC 
SST 
7.01 
44.97 
64.78 
0 
7.01 
44.97 
64.78 
3.81 
SST, SSS 
13.05 
13.47 
71.06 
6.28 
8.64 
15.68 
76.65 
15.68 
SST, SSHA 
6.19 
27.62 
76.54 
11.76 
4.26 
31.47 
75.77 
14.8 
SST, chi a 
17.42 
5.17 
75.99 
11.21 
11.80 
16.41 
68.61 
7.64 
SST, SSHA, chi a 
19.57 
0.87 
80.03 
15.25 
6.53 
26.94 
60.97 
0 
SST, SSS, SSHA 
11.18 
17.64 
84.64 
19.86 
-2.01 
44.03 
77.24 
16.27 
SST, SSS, chi a 
19.08 
1.84 
77.16 
12.38 
2.58 
34.85 
62.26 
1.29 
SST, SSS, SSHA, chi a 
18.39 
3.23 
71.60 
6.82 
1.11 
37.78 
70.00 
9.03 
