Zeidberg et al. The fishery for Loligo opalescem from 1981 through 2003 



55 



the 1990s, reflecting fishery participants from Alaska, 

 Washington, and Oregon. The most economically harm- 

 ful trend has been the substantial decrease in landings 

 during the second year of strong El Niiio events, and the 

 slight decrease in landings after weak ones. 



The initial impetus of performing the spectral analy- 

 sis was to determine if the squid were migrating to the 

 spawning grounds in relation to a lunar or tidal signal. 

 It is important to note that the spectral analysis with 

 CPUE and landings data (not shown) did not show that 

 squid recruit to spawning sites in a fortnightly cycle. 

 There was no 14-day period in any area. Spectral analy- 

 sis demonstrated periodicities for CPUE of Loligo opal- 

 escens on scales ranging from days to years. The most 

 common periods for all areas were annual. Varying from 

 315 to 390 days, annual cycles made up more than half 

 of the top ten signals in the analysis. The 4.5-year cycle 

 corresponds well with the El Nirio events of 1982-83, 

 1987. 1992, and 1997-98 (Hayward et al., 1999). In 

 each of these cases the CPUE anomalies were negative 

 (Zeidberg, 2003). The longest period was 7.5 years in 

 the MB and CC areas. There were evident leaps in the 

 mean CPUE based on mean CPUE ±5 months in MB 

 at mid-1988 and the end of 1995, when out-of-state fish- 

 ermen began to harvest squid in California (Zeidberg, 

 2003). Although these leaps may correspond to changes 

 in the biomass of the squid, they are more likely due to 

 enhancements in the capacity of the fishery to capture 

 squid as acoustic and communication technology has 

 improved. The 3.7-year period is probably a statistical 

 harmonic of the 7.5-year period. 



Paralarvae density index (PDI) can predict CPUE 



Zeidberg and Hamner (2002) have sampled the SCB 

 and SM areas for Loligo opalescens paralarvae since 



1999 and we used that data to create a paralarvae 

 density index (PDI). CPUE appears to be a better 

 indicator of stock abundance than landings data for 

 squid (Sakurai et al., 2000). Adults recruiting to the 

 fishery in November, measured in CPUE, can be pre- 

 dicted by linear regression from the PDI of February. 

 A regression of the CPUE data from the PDI data for 

 1999-2003 is not significant, but if 1999 is treated as 

 an outlier the remaining four points (2000-03) create 

 a regression that explains 97.8% of the variance. Our 

 1999 sampling of paralarvae may not be representative 

 of the fishery because it was the first sampling year 

 and the sampling sites were located farther offshore 

 than those sampled in 2000-03. In 1999 there were no 

 sites within 7.4 km of shore, where 76% of the paralar- 

 vae were captured in the following four years of sam- 

 pling. Despite these caveats, this method could provide 

 the first opportunity to manage California's market 

 squid fishery according to scientifically gathered bio- 

 logical indicators and with very few of the inherent 

 assumptions needed for many other types of forecast- 

 ing (Mangel et al., 2002). As the years of logbook data 

 accumulate, estimates of CPUE will be more closely 

 related to the actual biomass of the species. By the end 

 of February, we can have a prediction for the CPUE 

 for the following year's adult recruitment. Paralarvae 

 may be the best stage of the life cycle for a fishery 

 prediction because juveniles can escape trawls, fewer 

 assumptions need to be made than with estimates from 

 spawning females (Macewicz et al., 2004), and there 

 is sufficient time (6-9 months) to develop predictions. 

 These predictions could help managers set catch limits 

 and aid fishermen in deciding how to invest in gear for 

 the following season. 



In addition to our paralarvae sampling, CalCOFI 

 has sampled the waters of California for zooplankton 

 in a manner similar to ours since 1949. Paralarval 

 distributions for Loligo opalescens have been described 

 from these data (Okutani and McGowan, 1969). The 

 greatest difference between the two sampling efforts 

 is the number of stations that are in close proximity 

 to land. The majority of the paralarvae (76%) captured 

 by Zeidberg and Hamner (2002) were at stations less 

 than 8 km from shore, but there is only one CalCOFI 

 station at this proximity to land. After reviewing their 

 surveys and models of larval dispersal (Franks, 1992; 

 Botsford et al., 2001; Siegel, 2003), we predict that a 

 PDI calculated from CalCOFI samples will be substan- 

 tially lower than ours, but given the long time period 

 of the CalCOFI sampling program, any significant cor- 

 relations could be more powerful statistically than ours. 

 Furthermore, fishermen could be employed to perform 

 bongo tows for paralarvae in proximity to shore to com- 

 plement CalCOFI data. If the CalCOFI bongo net data 

 were sorted for Loligo opalescens paralarvae, and fisher- 

 men collected paralarvae nearshore, Monterey Bay and 

 southern California CPUE could be predicted months 

 in advance. Separate management of the two regions 

 would be necessary given the time lag of recruitment 

 (APR and OCT). 



