Hanselman et al.: Application of an acoustic-trawl survey design to improve estimates of rockfish biomass 
385 
Table 2 
Catch (kg), number of individuals, and mean fork length (cm) of fish and associated coefficient of variation (CV) for the top species 
caught during our experimental rockfish acoustic-trawl survey conducted in 2009 near Yakutat, Alaska. 
Common name 
Scientific name 
Weight 
(kg) 
Number of 
individuals 
Mean fork length 
(cm) 
Length CV 
(%) 
Pacific ocean perch 
Sebastes alutus 
16,603 
27,276 
32.3 
19 
Walleye pollock 
Theragra chalcogramma 
3110 
3988 
45.8 
12 
Shortraker rockfish 
Sebastes borealis 
2173 
426 
65.3 
15 
Arrowtooth flounder 
Atheresthes stomias 
1738 
1506 
37.6 
32 
Shortsp ine thornyhead 
Sebastolobus alascanus 
1020 
5131 
24.0 
27 
Dover sole 
Microstomus pacificus 
789 
963 
40.2 
15 
Sablefish 
Anoplopoma fimbria 
775 
292 
61.0 
20 
Dusky rockfish 
Sebastes variabilis 
426 
262 
46.1 
5 
Silvergray rockfish 
Sebastes breuispinis 
381 
167 
54.8 
13 
Jellyfish 
Chrysaora melanaster 
320 
187 
— 
— 
Other 
2836 
8444 
— 
— 
best alternative, estimated biomass and precision for 
comparison with our original field results. 
We examined the spatial structure of the S u along 
the entire trackline and fish densities from trawls us- 
ing classical method of moments sample variograms 
(Cressie, 1993). We also re-examined the densities of 
POP in trawls from an ACS experiment conducted in 
1998 (Hanselman et al., 2001), during which trawls 
were conducted at a higher spatial resolution (i.e., closer 
together) than they were in our 2009 study. We coars- 
ened the spatial resolution (upscaled the support) of the 
acoustic data by aggregating the S v values so that the 
distance between S c values was 1 km, which was the 
sampling resolution (support) of the trawl data (Atkin- 
son and Tate, 2000). We varied the maximum distance 
of spatial correlation until a clear range was identified. 
We then fitted different variogram models (spherical, 
circular, exponential, and linear) to determine the best 
shape of the variogram model. 
Results 
Field sampling occurred during daylight hours over 12 
days in August 2009. A total of 59 trawls were com- 
pleted, with 40 background trawls and 19 patch trawls 
(Fig. 2). The total weight of all species caught was 
30.1 metric tons (t). POP made up 55% of the overall 
catch from our study, followed by walleye pollock and 
shortraker rockfish (S. borealis) (Table 2). Mean CPUE 
of POP was 42,450 kg/km 2 in patch trawls and 7,475 
kg/km 2 in background trawls. The total trackline cov- 
ered was 1250 km; 112 km of this total was in patches 
where we trawled. Overall, about 20% of the trackline 
(230 km) was above the threshold S v but was either not 
long enough to invoke our patch definition or deemed 
untrawlable by the captain of the FV Sea Storm. A 
return to trawl inside patch stations added an additional 
travel cost of about 2% beyond the cost of trawling only 
at planned stations. The last 2 of the 59 trawls were 
conducted after our planned trackline was completed, 
and the stations of these 2 trawls were identified with 
an alternative patch definition (see discussion later in 
this section); therefore, we did not use them in our main 
analysis. 
Before comparing S v measurements with trawl densi- 
ties, we checked for normality of the data. The distri- 
bution of S v along the trackline was reasonably normal 
(Fig. 4), but trawl densities of POP were left-skewed 
and required transformation to approach normality. 
Hanselman and Quinn (2004) showed that power trans- 
formations were superior to the logarithm for POP sur- 
vey data. Applying the Box-Cox power transformation 
showed that the likelihood surface at different powers 
was relatively flat between 0.1 and 0.3. We chose to 
use the fourth-root of trawl CPUE because it showed a 
better residual pattern and had higher correlation with 
S v than did the logarithm and lower power transforma- 
tions. The relationship between S v and POP CPUE was 
relatively weak, particularly below -70 dB (Fig. 5). The 
relationship between POP CPUE and patch length was 
tenuous, with a low correlation coefficient (r=0.08). In 
some cases when our patch algorithm detected a patch, 
schools of POP appeared to dissipate or move off the 
seafloor in the time it took to return to the same loca- 
tion and set up a trawl (Fig. 6). 
The resulting biomass estimates were very similar 
among the different types of estimators (Table 3, Fig. 
7). All estimates of biomass from our study were much 
more precise, in terms of the coefficient of variation 
(CV), than estimates of biomass based on data for the 
same area from the NMFS GOA trawl survey conduct- 
ed in 2009 (Fig. 7). The bootstrap procedure yielded 
similar estimates of biomass and precision between the 
TAPAS and SRS estimators. If we included the 2 trawls 
conducted opportunistically off the planned trackline, 
on the basis of our alternative patch definition, the 
TAPAS design, with much higher biomass estimates, 
