Caldarone et al.: Nonlethal techniques for estimating responses of postsmolt Sa/mo salar to food availability 
267 
Hartman, 2005) but was not significant in the steelhead 
(r 2 = 0.02; Hanson et al., 2010). Pothoven et al. (2008) 
observed significant relations between conductor volume 
and total fat or dry mass in yellow perch ( Perea flaves- 
cens), walleye ( Sander vitreus), and lake whitefish ( Core - 
gonus clupeaformis), with r 2 values ranging from 0.62 to 
0.93. In our study, we also found conductor volume to be 
highly correlated with carcass protein content and total 
water (r=0.93 and 0.95, respectively) but more weakly 
correlated with total fat (r=0.74). Pothoven et al. (2008) 
suggested that significant correlation between published 
conductor volumes and body composition (g) is most 
likely due to biases imposed by the distance between the 
electrodes. The consistent placement of BIA electrodes 
on each fish results in the numerator of the conductor 
volume equation becoming a proxy for the size of the fish, 
and fish size is highly correlated with body contents (i.e., 
the larger the animal, the greater its total fat, water, 
protein, and ash content). In both our study (protein, 
fat, water) and Pothoven et al. (2008; yellow perch, fat 
and dry weight; walleye, fat), wet weight had a stron- 
ger relation to body composition (g) than did conductor 
volume. Neither Cox and Hartman (2005) nor Hanson 
et al. (2010) reported correlation results between wet 
weight and body composition (g). It would be valuable 
to know whether wet weight could estimate total fat, 
protein, and water content equally weil or better than 
BIA conductor volume in their studies as well. 
Prediction modeis 
In our study, the most parsimonious models for predict- 
ing body composition (g) all contained wet weight and 
fork length, and frequently a weight-length interaction 
term (Fulton’s K). Adding any combination of the nine 
BIA measures to size-based-only models increased the 
explanatory capabilities by less than 2%. Bosworth and 
Wolters (2001) estimated carcass fat content in channel 
catfish ( Ictalurus punctatus) using wet weight, R, and 
Xc as the predictor variables, resulting in an r 2 = 0.75. 
They determined that adding the BIA measures to a 
model containing wet weight only increased the pre- 
dictive capability by 71%; however, R and Xc in their 
model had not been corrected for the distance between 
the electrodes. Because these impedance measurements 
are highly dependent upon the distance the current 
must travel between the electrodes, they must be stan- 
dardized to distance when used in prediction equations 
(RJL Systems, http://www.rjlsystems.com/docs/bia_info/ 
principles/, accessed April 2008) or they simply become 
proxies for length. Therefore the Bosworth and Wolters 
(2001) carcass fat content model essentially contains 
size-related only variables — and size is highly correlated 
with content values. 
When body constituents are expressed as concentra- 
tions (e.g., percent wet weight), the values are less size- 
dependent. Pothoven et al. (2008) examined the capabil- 
ity of 4 models (3 of which contained BIA measures) to 
predict percent total fat in 3 fish species. Interestingly, 
all of the variables used in their models were size re- 
lated (body mass, total length, conductor volume, R par 
and Xc |)ar uncorrected for distance between electrodes). 
In their study, fat concentration within a species encom- 
passed a range of values (yellow perch: 2. 7-8. 7%; wall- 
eye: 6.0-18.2%; lake whitefish: 2.4-14.7%), but overall 
predictive capability of the most parsimonious model for 
each species was low (r 2 range: 0.17-0.53). 
In our study, all of the body composition (%WW) mod- 
els also had low predictive capabilities, with less than 
50% of the variability explained by any of the top 3 
models. Our most parsimonious models for TF% and 
TWa% contained only size variables (Table 4), whereas 
the CP% and growth rate models contained both size 
and either 2 or 3 BIA measures (Table 4). In the CP% 
models there was little added value of BIA measures 
to models containing only size variables (explanatory 
capabilities increased by <3.4%), and in the growth-rate 
models, adding two BIA measures did increase the r 2 of 
size-based-only models by -20%. However, the overall 
predictive capability of the growth-rate models was still 
less than 50%. 
In a recent study by Hartman et al. (2011), highly 
predictive models for estimating percent dry weight in 
coastal bluefish (Pomatomus saltatrix) were constructed 
by using BIA measures. The most highly predictive 
models for their fish (15°C, r 2 =0.86; 27°C, r 2 = 0.91) con- 
tained either phase angle or capacitance, plus RID and 
Xc/D. Ideally, standardizing R and Xc by the distance 
between the electrodes (D) would eliminate the effect 
of size on the impedance values; however, in our study 
we determined that even after standardization, R pa JD 
and Xc par /D were still highly correlated with size (wet 
weight, r>0.91, Table 5B). Correlations between size 
(wet weight) and candidate BIA predictor variables were 
not reported in the coastal bluefish study, and size (wet 
weight or length) was not tested as a predictor variable 
in any of the models. 
In our study, size continually emerged as a significant 
variable in all of the body composition models. To our 
knowledge, no experiment has been conducted which 
controls the effect of size well enough to determine the 
actual contribution of BIA measures to the estimation 
of body composition independent of size. Until this is 
better understood, it will remain unclear to what extent 
BIA measures can improve upon size-only estimates of 
body composition. 
Phase angle 
The use of BIA-estimated phase angle as a measure 
of fish condition has been proposed by Cox and Heintz 
(2009). They concluded that in the 5 species they stud- 
ied, fish with phase angles >15° were in better condition 
than those with phase angles <15°. Caution should be 
used before universally applying this 15°cut-off value. 
In their own study, Cox and Heintz (2009) observed 
12° phase angles in field-caught Pacific herring ( Clupea 
pallasii) with mass-specific energy contents of 7.15 kJ/g, 
and 15° phase angles in Pacific herring caught four 
months later with energy contents of 5.02 kJ/g. They also 
