Hanselman et ai.: Application of an acoustic-trawl survey design to improve estimates of rockfish biomass 
391 
Distance (km) 
Figure 9 
(A) Variogram of mean volume backscattering ( S tl ) from the vessel path (n = 669 mean values) sampled during our 
2009 acoustic-trawl survey. Line is spherical model fit: range (where spatial correlation ends) = 12.9 km, partial 
sill (explained variance) = 17.5 km, nugget (unexplained microscale variance) = 2.6 (B) Variogram of [CPUE] 0 - 25 
during the 1998 adaptive-cluster-sampling experiment (n = 147 trawls). The line is the linear model fit: range=7.5 
km, partial sill= 3.0 km, nugget=2.2. 
Table 5 
Comparison of 5 alternative methods of patch selection to the original design for a 3-trawl-length (~3.0 km) acoustic sampling 
window. 
Patch 
definition 
Description 
Selects above 
average CPUE 
Selects below 
average CPUE 
Error rate 
(%) 
Original 
80th percentile, 0.38 of the time 
14 
4 
22 
1 
90th percentile, 0.12 of the time 
7 
2 
22 
2 
80th percentile of the standard deviation of S ( , 
4 
1 
20 
3 
80th percentile of variance to mean ratio 
4 
1 
20 
4 
90th percentile of max 
6 
1 
14 
5 
80th percentile of 1/max S„xSD (S u ) 
4 
1 
20 
al., 2005). Beare et al. 3 found that using the length and 
species composition information from trawls to partition 
acoustic backscatter to species improved correlations. 
Mackinson et al. (2005) used a fuzzy logic approach to 
examine the relationship between acoustic backscatter 
and trawl CPUE, and they found that depth was a bet- 
ter predictor of trawl CPUE than was acoustic backscat- 
ter. For Alaskan groundfishes, species composition can 
be inferred relatively accurately by depth (Hanselman 
and Quinn 2004). Further work should focus on iden- 
tifying specific characteristics of acoustic backscatter, 
such as school shape, target strength, and school den- 
3 Beare, D. J., D. G. Reid, T. Greig, N. Bez, V. Hjellvik, O. R. 
Godp, M. Bouleau, J. van der Kooij, S. Neville, and S. Mack- 
inson. 2004. Positive relationships between bottom trawl 
and acoustic data. ICES CM (council meeting) document 
20Q4/R:24, 15 p. 
sity that would contrast rockfishes from co-occurring 
species. However, multivariate analyses have shown 
that distinguishing POP backscatter from walleye pol- 
lock backscatter is challenging (Spencer et al. 4 ). 
Increased precision for future applications of the 
TAPAS design could be attained in several ways. Im- 
proved correspondence between acoustic backscatter and 
trawl CPUE, for example, could be obtained from better 
partitioning of acoustic backscatter to species and quan- 
tifying the availability and vulnerability of a fish to 
these 2 sampling methods. Spencer et al. (2012) showed 
4 Spencer, P. D., D. H. Hanselman, and D. R. McKelvey. 
2011. Evaluation of echosign data in improving trawl survey 
biomass estimates for patchily-distributed rockfish. North 
Pacific Research Board Final Report 809, 110 p. [Avail- 
able from http://doc.nprb.org/web/08_prjs/809_final%20 
report_revised%20_2_.pdf, accessed September 2011.] 
