Zimmermann and Rooper: Comparison of echogram measurements for distinguishing seafloor substrates 
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seafloor slopes exceeding 5° to 8° caused significant 
substrate misclassification. In Ellingsen et al. (2002), 
effects from vessel motion may have been reduced or 
eliminated by working in calm seas. Our analysis 
of individual EMs revealed the mechanism whereby 
substrates may be misclassified in areas with slope 
and we would suggest that there is greater sensitivity 
with QTC VIEW than previously noted by von Szalay 
(1998) and von Szalay and McConnaughey (2002). The 
QTC IMPACT method of stacking multiple pings could 
potentially ameliorate the influence of slope-affected 
pings, but it could also create new substrate classes 
by combining normal and non-normal reflections. 
Although it is presumed that QTC IMPACT software 
could distinguish among substrates at a constant 
depth in a flat seafloor area with no vessel pitch 
and roll, this type of situation is not realistic for the 
NMFS bottom trawl surveys in the Gulf of Alaska and 
Aleutians Islands. If steep seafloor areas can be dis- 
tinguished from vessel motion through careful incor- 
poration of vessel motion measurements, slope may be 
considered as a substrate modifier or as a significantly 
different substrate, depending on the species of inter- 
est for which habitat is being defined. 
Assumption one: scale of measurements 
The QTC IMPACT method of PCA (without standard- 
ization of data) results in a higher amount of variance 
explained because it is based on a few variables with 
the highest variance, which are also highly corre- 
lated. Those EMs without much variance only make 
a minor contribution to the PCA solution; however, it 
remains unclear whether forcing EMs to vary through 
standardization — a process that could possibly include 
both discriminating (signal) and nondiscriminating 
measures (noise) of echo energy, timespread, and skew- 
ness (van Walree et al., 2005) — increases or decreases 
statistical power for discriminating substrate types. 
The user is left having to choose between conducting 
a nonstandardized PCA where nearly all variables are 
collinear or conducting a standardized PCA that may 
be based mostly on noise. Including fully collinear 
(e.g., the sums of) variables and correlated variables 
in a PCA does not provide additional discriminatory 
information, but it does change the results. Therefore 
users may find it beneficial to conduct an additional 
PCA without these collinear variables and determine 
how much the substrate groupings change. Perhaps 
not coincidentally, our findings that only three to six 
variables within each of the acoustic data sets were 
somewhat independent (provided discriminatory power) 
matches well with van Walree et al.’s (2005) descrip- 
tion of six acoustic algorithms and Kloser et al.’s (2001) 
description of four algorithms. Our results indicate that 
the EMs are not standardized before PCA, and because 
this is not mentioned in the literature, it may be an 
unexpected problem for users. The lack of standardiza- 
tion among collinear and correlated variables might 
partly explain why QTC IMPACT software typically 
requires only three eigenvectors to explain more than 
95% of covariance (Ellingsen et al., 2002; Legendre et 
al., 2002). 
Assumption two: first echo 
Surveys conducted in shallow water with high sam- 
pling rates and surveys conducted in deep water with 
low sampling rates (such as that of the NMFS 2003 
FV Gladiator) are equally vulnerable to accidentally 
including second, or later, seafloor reflections in QTC 
IMPACT analysis. For example, the RV Rangithi 1999 
and RV Pallasi 2004 data sets both required 9.4 m 
below the start of the first seafloor reflection to achieve 
251 samples, but the range of these data sets was shal- 
lower (Table 1). Because both of these data sets were 
recorded directly into QTC VIEW, the raw data could 
not be examined to determine if additional echoes were 
recorded or not. However, both data sets had charac- 
teristic drops in the values of the first group of EMs 
between approximately 9 and 4 m depth, indicating a 
probable increasing inclusion of the second echo with 
a decrease in depth (see Fig. 3). By 4 m in depth, both 
data sets should have included most of the second echo. 
The sharp increase in EM 1 just below 3 m in the RV 
Pallasi 2004 data set, at the shallowest depth, may indi- 
cate partial inclusion of the third echo. Thus accidental 
analysis of more than one echo with QTC IMPACT can 
cause strong depth-related influences and can create 
significantly different echogram measurements such 
that additional substrate classes could be created. To 
avoid such problems, users need to compare the depth 
range for their echogram measurement analysis (echo 
envelope) to the range of depths in their study area. 
Conclusions 
The need for a cost-effective approach to classify sea- 
floor substrates, in order to define EFH across areas 
such as the NMFS bottom trawl surveys of the Gulf of 
Alaska and Aleutian Islands, remains strong. Because 
of the unexpected problems with the QTC IMPACT 
processing steps and creation of EMs, it seems highly 
likely that QTC IMPACT users are producing substrate 
classifications based on problems implementing the 
software or analyzing the measurements. Although 
data-gathering or data-processing errors are common 
across all such analyses, there is little chance to correct 
such errors when using a black box system. Therefore 
for future projects more transparent analytical methods 
will be needed, such as the published algorithms in 
Kloser et al. (2001) and van Walree et al. (2005), for 
translating acoustic data into EFH. 
Acknowledgments 
We thank D. Urban for supplying the RV Resolution 
2003 data, I. Murfitt for the RV Pallasi 2004 data, 
