Hanselman et at: Application of an acoustic-trawl survey design to improve estimates of rockfish biomass 
389 
nal sampling algorithm revealed patches of acoustic 
backscatter that were not characteristic of rockfishes. 
Steadier and less intense than backscatter associated 
with rockfishes, these patches may have been caused 
by squid ( Berryteuthis spp.) or eulachon ( Thaleichthys 
pacificus). In addition, a substantial amount of walleye 
pollock was caught coincident with POP catches. The 
TAPAS design may perform better in multispecies situ- 
ations because of the relatively relaxed requirement of 
categorizing data into 2 groups, as opposed to the more 
involved effort of a statistical regression required for 
double sampling in a regression design. 
Differences in the portions of the water column sur- 
veyed by the 2 sampling methods also can lead to low 
correspondence between acoustic and trawl data. Rock- 
fishes can be closely associated with the seafloor and, 
perhaps, in the acoustic dead zone, but walleye pollock 
and other species are typically observed higher in the 
water column. We also noted the ephemeral nature of 
fish schools (Fig. 6), which may be attributed to re- 
sponses to vessel noise or to changes in the position of 
fishes in the water column for foraging. Diurnal and 
seasonal changes in the level of aggregation clearly 
could hinder the effectiveness of our acoustic algorithm 
in relation to fish CPUE. Changes of the vertical orien- 
tation of POP to the seafloor also could influence back- 
scatter and may have affected our acoustic algorithm 
(Freon and Misund 1999). 
When the field data from our study were re-analyzed 
with different patch definitions, we found that CPUE 
was more strongly related to acoustic backscatter in a 
window longer than the typical trawl distance — likely a 
result of the extremely fine spatial structure of schools 
or to the behavioral reactions of fishes to the initial 
pass of the FV Sea Storm over the patch (Mitson and 
Knudsen, 2003). If the spatial structure of schools was 
relatively narrow, then the trawl net may not have 
passed through the same school that was identified 
by the echosounder because of currents and imperfect 
tracking of the original vessel path (Ona and Godp, 
1990; Engas et al., 2000). Re-analysis revealed that 
the use of the 90 th percentile of maximum S v was more 
successful in identifying stations where rockfish CPUE 
was high and resulted in slightly more precise biomass 
estimates, compared with results from the original 
patch definition, despite a lower sample size. As with 
the analysis of Hanselman and Quinn (2004) with their 
ACS simulations, our re-analysis of the acoustic data 
showed that the TAPAS estimator can be improved 
when a high criterion of acoustic backscatter is used for 
the patch definition (i.e., additional sampling is invoked 
only in a few, high fish-density instances) and essen- 
tially outliers are removed from the random sampling 
portion of the ACS and TAPAS estimators. 
The TAPAS design incorporates aspects of both adap- 
tive sampling, which usually consists of a single sam- 
pling gear applied to a highly variable spatial distribu- 
tion, and double sampling designs that rely on sampling 
primary and auxiliary variables (Thompson, 2002). 
The TAPAS design provides one operational method 
Table 3 
Parameter estimates from 2 sampling designs, Trawl and 
Acoustic Presence/Absence Survey (TAPAS), and simple 
random sampling (SRS), with the use of 2 different patch 
definitions. Patch definitions are based on percentiles 
of mean or maximum volume backscattering ( S v ) from 
acoustic data collected during our 2009 acoustic-trawl 
survey. Rockfish densities and biomass estimates are 
given in metric tons per square kilometers (t/km 2) and 
metric tons (t), respectively. n=total sample size, 7=the 
number of patches, l'= the estimate of length (km) of total 
trackline in patches, L=the length of the entire trackline, 
Z) 0 =the mean background CPUE, D^the mean patch 
CPUE, B 0 =the background biomass, B ; =the patch bio- 
mass, B=the TAPAS estimate of total biomass (kg), B SRS = 
the SRS estimate of total biomass. SRS coefficients of 
variation (CVs) were calculated by using the full sample 
size {n). 
Parameter 
80 th percentile 
of mean S v 
90 th percentile 
of max S B 
N-I 
40 
41 
n 
57 
49 
i 
17 
8 
r 
93.6 
43.5 
L 
1251 
1251 
D 0 
7.48 
7.43 
D i 
9.74 
24.82 
B 0 
53,928 
55,898 
5684 
6734 
B 
59,612 
62,632 
CV B (analytical) 
34.6 
34.0 
CV B (bootstrap) 
34.5 
33.6 
Bsrs 
68,517 
68,517 
CV SRS (analytical) 
27.8 
30.0 
CV SRS (bootstrap) 
30.2 
31.9 
for implementing a double sampling for stratification 
design. The use of acoustics to stratify a survey area 
was generally recommended by Fujioka et al. (2007) and 
Hjellvik et al. (2007), with the difference that acous- 
tic backscatter is continuously monitored rather than 
sampled in discrete units. 
Results from our study and the ACS design attempted 
by Hanselman et al. (2003) highlight that even when 
focusing specifically on the abundance of rockfishes, it 
is difficult to survey stocks with high spatial variability 
that exist on both trawlable and untrawlable grounds. 
In the ACS surveys of Hanselman et al. (2003) special- 
ized tire gear was used, which made trawling on each 
cluster station possible, but made comparisons of CPUE 
impractical between those ACS surveys and surveys 
that used typical NMFS trawl gear. In our study, we 
used standard NMFS trawl gear; however, it could not 
be used in all observed patch stations. If POP were 
more abundant in some of these untrawlable patches 
and we had used different gear that would have allowed 
us to survey those patches, we may have had higher 
