Chen et at: A modeling approach to identify optimal habitat and suitable fishing grounds for Ommastrephes bartramii 
3 
mackerel (Scomber japonicus ) in the East China Sea 
(Chen et al., 2009) and in the study of the distribution 
of fishing grounds for purpleback flying squid ( Symlec - 
toteuthis oualaniensis) in the northwest Indian Ocean 
(Chen and Shao, 2006). 
Based on the previous results, it was found that O. 
bartramii, as a short-lived (l-year) species, is usually 
aggregated in the waters with favorable ranges of SST, 
SSS, SSHA, and chi a. Although the favorable ranges 
of these environmental variables for O. bartramii are 
known, a robust method to predict where O. bartramii 
will aggregate in the traditional fishing grounds from 
these variables is not yet available. It is important to 
develop a model to predict the occurrence of aggrega- 
tions of O. bartramii to reduce bycatch of untargeted 
species, reduce fuel costs, and improve efficiency of the 
fishery. 
Habitat suitability index (HSI) modeling can be used 
in combination with the geographic information system 
(GIS) technology to create maps important to fisheries 
management (Eastwood et al., 2001). HSI models are 
based on suitability indices that reflect habitat quality 
as a function of one or more environmental variables. 
Recently, HSI modeling methods have been successfully 
used to identify and forecast potential fishing grounds 
for bigeye tuna ( Thunnus obesus) (Feng et al., 2007; 
Chen et al., 2008b) and chub mackerel (Chen et al., 
2009). Because near real-time environmental variables 
such as SST, chi a, and SSHA can be easily measured 
by remote sensing, an HSI modeling approach has great 
potential for estimating abundance (which will be used 
for resource management), and for forecasting fishing 
grounds. 
The objectives of this study were to develop an HSI 
model to detect the potential fishing grounds for neon 
flying squid by using remote sensing data in combina- 
tion with fisheries data, and to find the optimal habitat 
for O. bartramii on their feeding grounds to provide a 
scientific basis for the management of this species. The 
environmental variables considered in this study includ- 
ed SST, SSS, SSHA, and chi a, all of which have been 
identified as critical to the distribution and abundance 
of O. bartramii in previous studies (e.g., Chen, 1997; 
Wang et al., 2003; Xu et al., 2004; Tian, 2006). 
Methods and materials 
Fishery data 
The area of 39-46°N latitude and 150-165°E longitude 
is an important traditional fishing ground for O. bartra- 
mii from August to November (Chen and Tian, 2005). 
Between 75% and 84% of the total catch has been landed 
in this area by Chinese mainland squid jigging fleets 
during the last decade (Chen et al., 2008a). Fishery 
data from 1999 to 2005 from this area were compiled 
monthly (Chinese Mainland Squid Technical Group, 
Shanghai Ocean University, Shanghai, China). These 
data, including squid catch per fishing day and fishing 
position, were georeferenced and grouped into a unit of 
0.5° x 0.5° latitude and longitude. 
We assumed no bycatch in the squid fishery (Wang 
and Chen, 2005) and that catch per unit of effort 
(CPUE) (tons [t]/day [d] ) of the squid jigging vessels 
is a good indicator of stock abundance on the fishing 
grounds (Chen et al., 2008c). The nominal CPUE in one 
fishing unit of 0.5°x0.5° was calculated as follows: 
CPUE ymi = (1) 
r ymi 
where CPUE ym = monthly nominal CPUE (t/d) at i fish- 
ing units in month m and year y; 
C ymi = monthly catch (t) at i fishing units in 
month m and year y; and 
F ymi - number of fishing days at i fishing 
unit in month m and year y. 
Satellite remote sensing data 
Physical and biological environmental data used to 
describe oceanographic conditions in our survey area 
included SST, SSS, SSHA, and chi a. Monthly SST data 
with a spatial resolution of 0.5°x0.5° were obtained 
from the Physical Oceanography Distributed Active 
Archive Center (PODAAC) of the National Aeronautics 
and Space Administration (NASA) website (http:// 
podaac.j pi. nasa.gov/DATA_CATALOG/index.html, 
accessed October 2008). Monthly SSS and SSHA data 
sets, both with a spatial resolution of 0.5°x0.5°, were 
downloaded from the IRI/LDEO Climate Data Library 
(http://iridl.ldeo.columbia.edu, accessed October 2008). 
Monthly chl-a level-3 standard map images with a 
spatial resolution of 9 km, from the “sea viewing wide 
field of view sensor (SeaWiFS), were obtained from the 
Goddard Space Flight Center on the NASA website 
(http://oceancolor.gsfc.nasa.gov/SeaWiFS/, accessed 
October 2008). 
Establishment of the HSI model 
The potential fishing grounds could be estimated through 
a habitat model combined with satellite-derived environ- 
mental factors. Fishing effort has been considered an 
index of fish occurrence or fish availability (Andrade and 
Garcia, 1999), and also successfully used in developing 
HSI models (Gillis et al., 1993; Swain and Wade, 2003; 
Zainuddin, et al., 2006; Tian et al., 2009). Therefore, we 
first analyzed fishing effort in relation to the above four 
environmental variables to identify the probability of O. 
bartramii availability. The probability, expressed as a 
suitability index (SI), was defined from the relationships 
between fishing effort and environmental variables. The 
highest probability value (SI=1) is associated with the 
fishing effort in a given interval of the environmental 
variables, which represents the most favorable envi- 
ronmental condition (Brown et al., 2000). The lowest 
probability value (SI = 0) indicates the lowest fishing 
