Zimmermann and Rooper: Comparison of echogram measurements for distinguishing seafloor substrates 
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Number of principal components 
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Figure 5 
Percent of variance explained in principal components analysis 
(PCA) conducted on the echogram measurements produced by 
QTC (Quester Tangent Corporation, Sidney, British Columbia, 
Canada) IMPACT™ software when the data were not standard- 
ized (QTC method ) and when the data were standardized 
(textbook method ) for the RV Resolution 2003 cruise (□), 
the C.C.G.S. RV John P. Tully 2002 cruise (O), the RV Pallasi 
2004 cruise (O), the RV Rangithi 1999 cruise (+), and the NMFS 
FV Gladiator 2003 shallow (x) and deep (■&) cruises. 
intervals versus first echo length), such that 
it does not function as users would expect for 
distinguishing substrate types. There are also 
several processing steps within QTC IMPACT, 
such as repeating the last sample of short pings 
(padding), reducing the strength of sections of 
pings that are too strong, and increasing the 
strength of sections of pings that are too weak, 
all of which may affect analyses. The propri- 
etary nature of the software and the internal 
processing steps have discouraged user criticism 
and examination of the QTC IMPACT generated 
data (Kloser et al., 2001). QTC IMPACT users 
should export, format, and carefully examine 
their 166 EMs before substrate classification 
in order to catch user-generated mistakes, such 
as accidentally including a second echo, and to 
identify and remove any constant or collinear 
EMs. After analysis of the EMs, users may be 
able to reduce the 166 EMs to fewer than 10 
without any loss of information, and compare 
these against depth, slope, and substrate types, 
if known, for further data-checking. The first 
assumption — that the scale or range of the data 
were appropriate for PCA — was disproved, and 
users may want to consider whether standard- 
izing is appropriate for their data. The second 
assumption — that QTC IMPACT only uses the 
first echo — is not necessarily true and published 
QTC IMPACT substrate classes may have been 
differentiated by the presence or absence of all or part 
of the second echo. 
Optimum substrate classification 
The inability to determine an optimum number of sub- 
strate classes for the shallow and deep FV Gladiator 
2003 data sets is a common problem in seafloor substrate 
analysis and is not a critique of the particular A-means 
method within QTC IMPACT software. Our echosounder 
data could have been too noisy, too coarse, too affected by 
sea-state or seafloor slope, or our trawl sites could have 
been too variable or too constant for determining sub- 
strate classes. Instead, our results, with corroborations 
from independent data sets, indicated the importance of 
analyzing the echogram measurements before any PCA 
and A-means analysis so that depth-related and slope- 
related errors, second echo or echo envelope errors, and 
variable range or collinearity errors could be caught. The 
pros and cons of the QTC IMPACT method of A-means 
partitioning have already been thoroughly discussed. 
It was criticized by Legendre et al. (2002) who offered 
a new A-means method based on Euclidean distance. 
Preston and Kirlin (2003) responded by defending and 
elaborating on their A-means clustering method, which 
is based on Mahalanobis distance, and citing successful 
QTC IMPACT substrate-typing projects (Anderson, 2001; 
Morrison et al., 2001; Anderson et al., 2002; Ellingsen 
et al., 2002). Legendre (2003) offered additional criti- 
cism of the QTC IMPACT A-means clustering method 
and added that the QTC IMPACT method of cluster- 
ing, based on only the first three principal components 
(PCs), was strongly influenced by depth, since his PCI 
was strongly related to depth, as opposed to a solution 
that would include a greater number of PCs. Clearly the 
A-means method for distinguishing substrate classes is 
important, and strongly linked to the data values that 
feed into it. 
Examination of the data and the variability 
and covariance of echogram measurements 
This exploration of the 166 EMs provides the first 
description of the QTC IMPACT data set that is used for 
seafloor substrate classification. Before this description, 
only three EMs were known to be invariant and the rest 
were highly collinear (Legendre et al., 2002). These EMs 
were known to carried limited information and were 
highly redundant (Ellingsen et al., 2002). Researchers 
collecting data directly into QTC VIEW, such as cor- 
roborating data from different agencies, did not have 
the additional processing step of importing the data 
from EchoView®, and were probably unaware of the 96 
dB dynamic range required for QTC IMPACT software. 
Therefore the effect of clipping portions of pings that 
were too loud, increasing the sound level of ping portions 
that were too quiet, or adding or subtracting a constant 
amount of sound to entire ping data sets (gain adjust- 
ment), was not widely reported in the literature. Only 
Anderson et al. (2002) mentioned experimenting with 
