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Fishery Bulletin 1 10(4) 
Table 4 
The original method used in our 2009 acoustic-trawl survey and proposed alternative methods for selection of “patches,” or 
areas where catch per unit of effort may have been high, compared with other survey areas, on the basis of acoustic backseat - 
ter over a sampling window of 3 trawl lengths. Patch definitions were based on a threshold of mean volume backscattering ( S v ). 
Alternatives were created to maximize the strength of the relationship of S',, to CPUE and improve survey precision. 
Original patch definition 
The S u was computed for each 100-m cell within a moving window of 31 cells or 3.1 km. A patch was defined when the 
proportion of these cells exceeding an S v value of -65.6 dB was greater than 0.39 (the 80th percentile of the backscatter data 
collected in the Yakutat, Alaska, area in 2005 aboard the FV Sea Storm). 
Alternative 1 
Higher field threshold definition 
To account for the uniform and weak nonrockfish backscatter encountered in the field, the S threshold was increased to -61.4 
dB from the value used in the original method. The threshold for the moving proportion was lowered to 0.13. These values 
were computed from the 90 th and 50 th percentiles of our field data, respectively. The rationale for this definition was to detect 
patches when the acoustic backscatter was more variable but stronger than the backscatter detected as patches with the 
original patch definition. 
Alternative 2 
Standard deviation of S v 
To capture the tight intermittent clustering of rockfish schools, we used the following threshold: the standard deviation of S v 
was above the 80 th percentile. The rationale of this definition was to capture some distributional properties associated with 
rockfish acoustic backscatter. 
Alternative 3 
Variance to mean ratio of S u 
To remove uniform, diffuse acoustic backscatter and account for tight intermittent clustering of rockfish schools, we used the 
following threshold: the variance-to-mean ratio was above the 80 th percentile. The rationale of this definition was to identify a 
patch when the variance-to-mean ratio moved far above 1 (e.g., departing from a Poisson distribution toward a hypergeometric 
distribution). 
Alternative 4 
Maximum <S ( , 
If the survey was conducted in a depth stratum and area where the target species was abundant, it was assumed that pulses in 
maximum S v should reflect the dominant species. For this alternative, the 90 th percentile of maximum S v was used. 
Alternative 5 
Maximum S v and standard deviation of S v 
This method refined Alternative 4 by adding variability into the criterion in a multiple regression. The rationale of this 
definition was similar to the rationale of Alternative 2. 
POP densities in our patch trawls. When comparing our 
estimates with assessments of Hanselman et al. (2003), 
we found that the CV on mean CPUE was lower at the 
planned stations in our study than in the SRS portion 
of the ACS study. Unlike the bimodal bootstrap distri- 
bution of the SRS estimates in Hanselman et al. (2003), 
a relatively Gaussian distribution resulted when boot- 
strapping the TAPAS and SRS estimators. Both designs 
have the disadvantage of having a variable sample size, 
but both have the advantage of completing a survey in 
a single pass through a study area. The TAPAS design 
imposed a small additional cost for travel time because 
our vessel had to return to trawl a random location in a 
patch, but the daily number of trawls conducted was not 
affected. The ACS and TAPAS designs are both more 
efficient than some of other two-stage designs that re- 
quire the completion of an initial random sample before 
the second stage can begin. Another challenge with field 
studies of spatially variable species is that performance 
of survey designs depends highly on the fish densities 
encountered in a given survey. 
Previous attempts to improve the correspondence 
between acoustic backscatter and trawl CPUE have 
focused on partitioning the acoustic backscatter to spe- 
cies (Mackinson et al., 2005) and quantifying relative 
catchability of these 2 sampling methods (McQuinn et 
