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Fishery Bulletin 113(2) 
hatching was negatively correlated with squid catches 
in the following fishing season (Waluda et al., 1999), 
the catches would be higher after a lower November 
AAO 2 years previously. 
On the other hand, factors other than SST variabil- 
ity, such as ocean productivity, current variability, and 
mesoscale oceanographic processes are almost certain 
to be important in their influence on squid recruitment 
(Waluda et al., 2004). The relationship between the AAO 
and precipitation in southeastern South America has 
been examined (Silvestri and Vera, 2003; Vasconcellos 
and Cavalcanti, 2010), and precipitation indicated by 
freshwater input and stream flow can affect reproduc- 
tion and community structure in the marine environ- 
ment (Lloret et al., 2004). Moreover, the AAO, like the 
SOI in the South Pacific, reflects regional atmospheric 
circulation patterns. The AAO can affect current pat- 
terns In the southwest Atlantic, and, therefore, alter 
the physical habitats of Argentine shortfin squid with 
a time lag of more than 1 year. However, more informa- 
tion is necessary to clarify such large-scale complexity 
in atmospheric and marine ecosystems. 
Cephalopod biomass changes quickly throughout 
a year and interannually because of environmental 
fluctuations. Squid fisheries are well known for their 
highly variable recruitment and catches, and this in- 
consistency creates uncertainty for resource manag- 
ers and the fishing industry because of increased risk 
of stock collapse (Peel and Jackson, 2008). Prediction 
of abundances based on environmental variables be- 
fore the fishing season could be an important way to 
make management decisions more precise. The SST, 
subsurface seawater temperature from depths of 5 m, 
and SOI have been used as important environmen- 
tal factors to predict squid abundance. The results 
of this study indicate that atmospheric circulation 
patterns must be considered in evaluations of the 
relationship between environmental factors and the 
abundance of Argentine shortfin squid. The 4-para- 
mater model developed in this study explained 83% 
of the fluctuation in abundances. With a lag time of 
2 years in the parameters, this model may make it 
possible to predict the CPUE of Argentine shortfin 
squid 2 years before a fishing season — a strategy that 
would be useful for the management of the fishery of 
this species of squid. 
Acknowledgments 
The authors are indebted to the Overseas Fisheries 
Development Council of the Republic of China for the 
collection and maintenance of logbook data. We thank 
C. Hsieh of National Taiwan University for providing 
valuable comments on this manuscript. Financial sup- 
port was provided in part through a project from the 
Fishery Agency, Council of Agriculture, Republic of 
China. Appreciation also is extended to an assistant, 
K Chang, at National Taiwan University for help in 
conducting the project. 
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