Hanselman et al : Applications in adaptive cluster sampling of Gulf of Alaska rockfisfi 



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144"W 



I43°W 



Ma-w 



14 I '-'W 



14()°W 



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59°N 



Figure 2 



Map of sampling area in the Gulf of Alaska on the Uniinak 99-01 adaptive sampling cruise. "R" symbols 

 are the initial random tows for the criterion phase, "r" symbols are random stations in the survey phase, 

 "A" symbols are adaptive cluster samples. 



Three methods were formulated for determining a fixed 

 criterion value c of POP catch-per-unit-of-effort ( CPUE). ( 1 ) 

 We combined and calibrated past survey and fishing data 

 to provide the anticipated distribution of CPUE in the 1999 

 survey. Then we calculated the SO"^*^ percentile of that dis- 

 tribution as the criterion value. Our rationale was that this 

 value would correspond to that obtained from order statis- 

 tics. (Three networks were sampled in 1998; therefore the 

 criterion value was set to the 4'*^ highest of the ordered 15 

 initial tows, which corresponded approximately to the 80'^ 

 percentile. ) ( 2 ) We used the mean CPUE of past survey and 

 fishery data because when we compared the 80"^ percentile 

 criterion against the 1998 ACS survey's data, the sampling 

 would have resulted in primarily edge units. (3) After a 

 representative random sample was taken across the entire 

 area in 1999, we would use the initial mean CPUE for the 

 criterion value for the return trip. The rationale for using 

 mean CPUE above is that in an aggregated population, 

 the majority of the tows would be less than the mean. The 

 actual values of the criterion chosen under each alternative 

 are described in the results. 



We chose the SR-RE criterion to be the mean CPUE of 

 initial tows. We assumed this was a reasonable criterion 

 value because if the population of SR-RE were somewhat 

 uniform, a lower value would result in too much ACS, but 



mean CPUE would still be low enough to allow higher cri- 

 terion values to be examined. Although we concentrated on 

 evaluating criterion alternatives for POP, we present the 

 SR-RE data to illustrate that different levels of aggregation 

 could affect how much can be gained with ACS in terms of 

 precision and efficiency. 



A major problem in applying adaptive sampling is that 

 sampling may continue indefinitely because of a low crite- 

 rion value. To limit the amount of adaptive sampling, an 

 arbitrary stopping rule of S levels was imposed. For those 

 strata where the cross pattern of adaptive sampUng was 

 used (POP), the stopping rule was S = 3 levels, allowing for 

 a maximum of 24 adaptive tows around each high-CPUE 

 random tow (Fig. 1). For the strata with the hnear pattern 

 of adaptive sampling (SR-RE), the stopping rule was S = 4 

 levels, for a maximum of eight adaptive tows around each 

 high-CPUE random tow. This stopping rule differs from 

 that of the previous year in which we used a stopping rule of 

 six because we believed that the possible 30-km difference 

 between the ends of the networks was too large for efficient 

 sampling (Clausen^). In addition, no adaptive sampling ex- 

 tended beyond a stratum boundary. The result of adaptive 

 sampling around each high-CPUE tow was a network of 

 tows that extended over and, in some cases, delineated the 

 geographic boundaries of a rockfish aggregation. 



