Rooper and Martin: Comparison of indices of abundance with biomass estimates from trawl surveys 
27 
Table 4 
Correlations (r) among habitat variables for the combined Gulf of Alaska (1996-2009) data sets based 
hauls. 
on 4475 bottom trawl 
Shrimp 
abundance 
Bottom 
temperature 
Bottom depth 
Local slope 
Coral and 
sponge abundance 
Bottom temperature 
-0.008 
Bottom depth 
0.055 
-0.372 
Local slope 
-0.083 
-0.219 
0.545 
Coral and sponge abundance 
-0.013 
-0.061 
0.035 
0.125 
Thermocline depth/bottom depth 
-0.125 
0.140 
-0.407 
-0.212 
0.068 
was removed to produce a CPUE (in no. of rockfish/ha) 
for each year. In the case of Pacific ocean perch, back- 
transformed juvenile and adult CPUEs were summed to 
obtain an annual abundance estimate for that species. 
Because the GOA bottom trawl survey is a stratified 
random survey, the comparable design-based CPUE esti- 
mates (kg/ha), as well as their variances, were calculat- 
ed according to the formula of Wakabayashi et al. (1985). 
These CPUE estimates were calculated for each year of 
survey data and were expanded over the survey area to 
estimate the total biomass used in stock assessments 
in the GOA. These biomass estimates and their vari- 
ances were compared to the model-derived abundance 
indices for each species by using linear regressions. 
Results 
Cross correlations among variables used to predict rock- 
fish presence, absence, and abundance were not large 
in most cases (Table 4). The strongest correlation was 
between the local slope and bottom depth variables and 
was probably indicative of larger slope values at and 
near the continental shelf break where depths increased. 
Bottom temperature and the thermocline-depth-to-bot- 
tom-depth ratio were also marginally correlated with 
bottom depth. The remaining variables were generally 
not strongly correlated (r 2 <0.05). 
Models of rockfish distribution fitted very poorly 
(r 2 <0.05) for dusky rockfish (S. uariabilis), northern 
rockfish (S. polyspinis), and harlequin rockfish ( S . var- 
iegatus). The poorly fitted species appeared to be the 
result of a poor ability to predict presence, so that these 
species were not present at >80% of the trawl hauls 
where they were predicted to occur (Table 5). This find- 
ing may be a result of uneven sampling of their pre- 
ferred habitat. For example, northern rockfish, dusky 
rockfish, and harlequin rockfish are all known to prefer 
rocky areas that are largely inaccessible to survey bot- 
tom trawl gear. 
For the five remaining species, the method was rea- 
sonably accurate in predicting the presence or absence 
of a species at a trawl station (Table 5). Presence or 
absence for these five species was predicted correctly in 
>60% of the bottom trawl hauls. For these species, the 
variance explained by abundance models ranged from 
an r 2 =0.22 for juvenile Pacific ocean perch LCPUE to an 
r 2 = 0.66 for shortspine thornyhead (Table 6). Compari- 
sons of the residuals from the models to the normal dis- 
tribution indicated that the residual errors were similar 
to a normal distribution for these five species (Fig. 2). 
Local slope was significant in the best-fitting models 
of shortspine thornyhead, shortraker rockfish, rough- 
eye and blackspotted rockfish, sharpchin rockfish, and 
both juvenile and adult Pacific ocean perch, and it had 
considerable explanatory power for four of the six best- 
fitting models. Depth was also an important variable 
included in four of the six best-fitting models for these 
species. Coral and sponge abundance was significant in 
all the best-fitting models, although shortspine thorny- 
head LCPUE was negatively correlated with coral and 
sponge abundance (Fig. 3). Thermocline depth to bottom 
depth ratio was positively correlated with abundance 
of adult Pacific ocean perch and sharpchin rockfish 
LCPUE and was negatively correlated with juvenile Pa- 
cific ocean perch, shortspine thornyhead, and rougheye 
and blackspotted rockfish LCPUE, and was insignifi- 
cant in the shortraker rockfish model. The effect of this 
variable was also relatively weak in most cases (Table 
6). Shrimp abundance was included in the best-fitting 
models for those species that consume shrimp, albeit in 
a nonintuitive fashion for shortraker and rougheye and 
blackspotted rockfish (Fig. 3). 
The spatial patterns in the residuals did not reveal 
significant spatial structure remaining in the data after 
the modeling was completed. The increase in the cor- 
relation coefficient was marginal (<4%) when the krigecl 
surface values were added to the predicted values at 
each bottom trawl survey point. The spatial structuring 
was weak, indicating that high catches of most rockfish 
species were very patchy and that catches from the 
closest neighboring tows could be very different. The 
distances between stations at which the spatial autocor- 
relation in the data was maximized were small (ranging 
from 9 km for juvenile Pacific ocean perch to 33 km for 
rougheye and blackspotted rockfish). Thus, the scale of 
the patchiness of the data was probably much less than 
could be captured by the bottom trawl data. 
