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Fishery Bulletin 1 10(4) 
performed better than the SRS design (CV=27% vs. 
CV=34%). 
We examined our results with respect to the variables 
that would have produced a better correlation with 
trawl CPUE. The weak relationship between S v and 
POP CPUE was obtained when comparing only for the 
length of the trawl trackline (offset for trawl distance 
-90 
-80 
-70 
-60 
-50 
S„(dB) 
Figure 4 
Distribution of values of mean volume backscatter- 
ing (S u ) for 100-m segments over the trackline (n = 12,998 
segments) surveyed during our 2009 acoustic-trawl 
survey. The dashed line is a density plot of a normal 
distribution with the same mean and standard 
deviation. 
□ Background 
♦ Patch 
30 
- 25 
- 20 
- 15 
□ ♦ 
D ♦ V? 
□ + □ 
£> J □ 
□ 0 □ cf® □ 
□ 
□ 
□ □ 
-90 
-85 
-80 
-D- 
-75 
Mean S v (dB) 
- 10 
-70 
-65 
-60 
Figure 5 
Fourth-root transformed Pacific ocean perch ( Sebastes alutus ) catch 
per unit of effort (CPUE) versus mean volume backscattering {SJ 
per trawl from our 2009 acoustic-trawl survey. Light gray squares 
indicate background stations, and black diamonds indicate patch 
stations. 
behind the vessel). Higher correlations between acoustic 
backscatter and trawl CPUE resulted when acoustic 
backscatter was calculated from segments that were 
centered at the trawl track and 3-5 times the length 
of the trawl trackline than from segments that were 
only the length of the trawl trackline. We derived 4 
new patch definitions, using a 3 -trawl-length sampling 
window (~3 km), in addition to the patch definitions 
we used in the field (Table 4). We show results as if we 
had used the patch definition with the best relationship 
between S v and POP density in the field. 
Comparing these patch definitions, we found that 
the strongest predictor of POP CPUE was the one that 
used the 90 th percentile of maximum S v in a 3-trawl- 
length sampling window, which approximated the win- 
dow we used for our 2009 survey (Fig. 8). This sampling 
window also gave the lowest error rate in identifying 
areas of below-average CPUE as a patch station when 
they should not be (Table 5). The standard deviation 
of the 3-trawl-length sampling window also performed 
reasonably well. Alternative 5, one of the alternative 
patch definitions (Table 4), was attempted to combine 
backscatter variability and maximum S v , but it did not 
perform better than maximum S v alone. The addition 
of depth as a variable to any of these alternatives in 
a multiple regression yielded minor, insignificant im- 
provements to the model. 
As a basis for a modified patch definition, we re-ana- 
lyzed the acoustic data using an S v criterion of -58.11 
dB derived from the 90 th percentile of the maximum S v 
from the original 2005 FV Sea Storm data in our 31-cell 
window. Only 8 of the previous 19 patch stations were 
located in patches under this new definition. 
Because of this smaller sample size, SRS 
estimates were less precise with this new 
patch definition than with the original patch 
definition. However, despite the smaller sam- 
ple size, the new threshold for TAPAS did 
yield a slightly improved CV than the CV 
obtained with the original threshold (Table 
3). Overall biomass estimates were slightly 
higher, and all measures of precision yielded 
similar results (Table 3). 
Variogram analysis of the S v measure- 
ments showed strong spatial correlation 
at the spatial resolution of the trawl data 
(Fig. 9A). Variogram analysis of the values 
of trawl CPUE collected during our study 
revealed no appreciable spatial structure, 
likely because the trawls were relatively far 
apart (146 km on average). Alternatively, 
we compared the S v measurements from our 
2009 study with the values of CPUE col- 
lected during an ACS experiment conducted 
in 1998 (Hanselman et al. 2001); CPUE data 
were collected at a finer scale (27 km on 
average) in the ACS experiment than in our 
study (Fig. 9B). We fitted a spherical model 
to the S c measurements and a linear model 
to the trawl CPUE on the basis of visual fit 
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