12 
Fishery Bulletin 108(1) 
CPUE of O. bartramii increased from 1.44 t/d (0-0.2 
of HSI) to 3.01 t/d (0. 8-1.0 of HSI) and that the im- 
provement in HSI occurred from August to October, 
1999-2004. This approach, however, is not equivalent to 
testing the accuracy of the HSI model for predicting the 
quality of habitat for a species (Wakeley, 1988). In the 
evaluation of habitat quality, it is important to capture 
both the habitat characteristics and habitat selection in 
the linkage between physical environments and habitat 
preference of target species because only a precise HSI 
model can yield a reliable assessment (Chen, et al., 
2008b). However, the uncertainty associated with the 
HSI model predictions usually results from the degree 
of reliability of the SI curve, input data, and the HSI 
model structure (Chen et al., 2009). 
The AMM-based HSI modeling approach used in the 
present study was generally successful for its intended 
use in mapping habitat and forecasting fishing grounds 
when the three environmental variables (SST, SSHA, 
and chi a) were used, whereas GMM may be appropriate 
for determining potential fishing grounds when only one 
environmental variable (SST) is used. This result shows 
that SST is the most important environmental variable 
in the HSI modeling for neon flying squid. Different 
structures of HSI models would lead to different re- 
sults, and the optimum HSI is different when different 
combinations of environmental variables are considered. 
Chen et al. (2009) also selected the AMM model as the 
optimum HSI model combined with four environmental 
variables (SST, SSHA, SSS, and chi a) in studying habi- 
tat suitability for chub mackerel in the East China Sea. 
Other different approaches are also used in addressing 
fish-habitat modeling. Norcross et al. (1997) modeled 
habitat suitability for flatfish by using a regression tree 
analysis in Alaska, and Swartzman et al. (1992) and 
Stoner et al. (2001) used generalized additive models for 
modeling flatfish distribution in the Bering Sea and for 
winter flounder ( Pseudopleuronectes americanus) in New 
Jersey, respectively. Le Pape et al. (2003) characterized 
the distribution of common sole ( Solea solea), using a 
genera] linear model. Eastwood et al. (2001) applied 
regression quantiles and GIS procedures to model the 
spatial variations in spawning habitat suitability for 
S. solea. The model output better reflects theoretical 
findings on the spatiotemporal nature of the species’ re- 
sponse to preferred environmental conditions. The AMM 
model with three environmental variables (SST, SSHA, 
and chi a) was considered to be the most parsimonious 
model in this study. However, we may need to conduct 
more studies for estimating HSI using other methods. 
Some of these methods may include allocating different 
weights for different environment variables in develop- 
ing HSI models and considering more environmental 
variables that may influence O. bartramii distributions, 
such as water temperatures at different depths, vertical 
structure of water temperature, and currents. 
HSI models can be applied to identify potential fish- 
ing grounds, but an optimal strategy for the squid fish- 
ermen in searching for fishing grounds would be to 
target an area with high habitat-suitability indices 
(>0.6) yielded from the AMM model. A dynamic near 
real-time habitat model incorporating more environ- 
mental variables, such as currents, fronts, winds, and 
other environmental variables, may further improve the 
process of identifying potential fishing areas. 
Acknowledgments 
We thank the Shanghai Ocean University for provid- 
ing the catch data, and NASA and The International 
Research Institute for Climate Prediction (IRI/LDEO) 
Climate Data Library of Columbia University for provid- 
ing the environmental data. This study is supported by 
National 863 project (2007AA092201), National Nature 
Science Foundation (NSFC40876090), the Program for 
New Century Excellent Talents in University (NCET- 
06-0437), and Shanghai Leading Academic Discipline 
Project (Project S30702). 
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