380 
Fishery Bulletin 1 10(4) 
Figure t 
Biomass estimates, shown in kilotons (kt), for Pacific ocean perch 
( Sebastes alutus, POP) determined from National Marine Fisheries 
Service groundfish trawl surveys conducted in the Gulf of Alaska. 
Error bars are approximate 95% confidence intervals. 
ducted in 1999 was driven by several very 
large catches, out of >800 trawls, that result- 
ed in extremely imprecise estimates (Fig. 1). 
In addition to having variable spatial distri- 
butions, some rockfish species have an affin- 
ity for rocky habitat, school semipelagically, 
and use different habitat types by size class 
(Stanley et al., 2000; Zimmermann, 2003; 
Rooper et al., 2010). These factors contribute 
to high sampling variability and demonstrate 
the need for examining alternative sampling 
designs or other technologies to improve sur- 
vey estimates of biomass (Godp, 2009). 
The difficulty of surveying rockfish popula- 
tions has been studied by using traditional 
survey designs like SSRS for some time (e.g., 
Lenarz and Adams, 1980). More recently, 
several attempts to improve survey precision 
for Alaskan rockfish species have been made 
by using alternative sampling designs. The 
utility of ACS has been examined in several 
studies (Hanselman et al., 2001; 2003). Many 
recent attempts have been made to use con- 
currently collected acoustic data to improve 
abundance estimation for demersal species 
(Ona et al. 1 ; Hanselman and Quinn, 2004; 
McQuinn et al., 2005; Fujioka et al., 2007). This subject 
also was the focus of a European-Union-funded proj- 
ect (combining acoustic and trawl surveys to estimate 
fish abundance, CATEFA; Hjellvik et al., 2007). These 
studies showed improvements in survey precision with 
the use of various measures, including accuracy and 
travel costs, but none of the survey designs were much 
more precise than that of a design that was stratified 
optimally for a particular species. For Pacific ocean 
perch (POP) in the Gulf of Alaska (GOA), Krieger et al. 
(2001) showed a relatively strong relationship between 
catch rates and raw acoustic backscatter in a small 
study area. Acoustic data were collected sporadically 
during the NMFS GOA trawl surveys between 2001 
and 2004 (Hanselman and Quinn, 2004) and have been 
collected consistently from 2005 to the current study 
(2012). Several studies have correlated these acoustic 
data with trawl catch for rockfishes (Hanselman and 
Quinn, 2004; Fujioka et al., 2007) and walleye pollock 
(Theragra chalcogramma) (von Szalay et al., 2007). 
Although much of the previous research has focused 
on combining results from trawl surveys and acoustic 
surveys into a single biomass estimate by assessing 
their relative catchabilities, the focus of our study was 
to attempt to use acoustic data to improve a traditional 
trawl survey design. 
Our objective was to test the hypothesis that the use 
of acoustic data in real time in the field to delineate 
areas with higher trawl-survey catch per unit effort 
1 Ona, E., M. Pennington, and J. H. Volstad. 1991. Using 
acoustics to improve the precision of bottom-trawl indices 
of abundance. ICES Council Meeting (CM) document, 
1991/D:13, 11 p. 
(CPUE) of POP, relative to other survey areas, could 
increase precision of biomass estimates from trawl sur- 
veys. To test this hypothesis, we employed an experi- 
mental sampling design, the Trawl and Acoustic Pres- 
ence/Absence Survey (TAPAS) (Everson et al., 1996). 
This design is a variant of the double sampling design 
(Thompson, 2002) and acoustic backscatter data are 
used to estimate the presence and size of areas, or 
“patches,” where CPUE may be high, compared with 
other survey areas, and to estimate the proportion of 
the total area classified as patches. Trawls are con- 
ducted at stations randomly selected before a cruise 
(planned stations) and in the acoustically detected high- 
CPUE patches identified during a cruise. The rationale 
of this design is to reduce sampling variability by al- 
locating more sampling effort in the areas of higher 
CPUE. If high-CPUE areas can be correctly identified 
with acoustic backscatter, it should be possible to es- 
timate biomass more efficiently. As with other double 
sampling designs, a critical assumption is that the 
auxiliary variable (e.g., acoustic backscatter) shows a 
strong correlation with the primary variable (e.g., trawl 
CPUE). We believe our study describes the first field 
application of this TAPAS design. 
Materials and methods 
Field methods 
The study area for our 2009 field experiment was chosen 
because we had prior CPUE and acoustic data from the 
NMFS GOA trawl surveys and CPUE data from a prior 
ACS experiment (Hanselman et al., 2003). We confined 
