24 
Fishery Bulletin 110(1) 
Species of rockfish examined 
their distribution where they 
Table 2 
in the habitat modeling analysis of the Gulf of Alaska bottom trawl survey data and the portion of 
are commonly found according to Love et al. (2002). 
Common name 
Species name 
Common distribution range 
Dusky I'ockfish 
Sebastes variabilis 
Southeastern Alaska through Eastern Aleutian Islands 
Sharpchin rockfish 
Sebastes zacentrus 
Southern California to Kodiak Island 
Pacific ocean perch 7 
Sebastes alutus 
Northern California through Aleutian Islands and Bering 
Sea 
Rougheye and blackspotted rockfish Sebastes aleutianus and 
S. melanostictus 
Central Oregon through Aleutian Islands and Bering Sea 
Harlequin rockfish 
Sebastes variegatus 
British Columbia through the Gulf of Alaska 
Northern rockfish 
Sebastes polyspinis 
Central Gulf of Alaska through the Aleutian Islands and 
Bering Sea 
Shortraker rockfish 
Sebastes borealis 
British Columbia through Aleutian Islands and Bering Sea 
Shortspine thornyhead 
Sebastolobus alascanus 
California through Aleutian Islands and Bering Sea 
1 This species was divided into juvenile and adult catch components with each modeled separately. 
to south and from west to east) for Pacific ocean perch, 
shortspine thornyhead, rougheye and blackspotted rock- 
fish, and shortraker rockfish (S. borealis ) (Table 2; Love 
et ah, 2002); therefore it is unlikely that geographic po- 
sition had a strong influence on the presence or absence 
of these species in this range. The longitude variable 
was therefore not used for these species. 
The cumulative distribution function (CDF) of rock- 
fish abundance was calculated over the range of depth, 
temperature, and longitude variables to determine the 
niche dimensions of each species. From the original 
3394 trawl hauls from 1999, 2003, 2005, 2007, and 
2009, replicate tows were chosen (with replacement) 
and the 5th and 95th percentiles of the cumulative 
distribution function were computed for each vari- 
able. This process was simulated 100 times for each 
of the variables (depth, temperature, and longitude). 
The average 5th and 95th percentiles were computed 
from the simulations for each variable, and the trawl 
haul tows occurring outside this range (below the 
5th percentile or higher than the 95th percentile for 
depth, temperature, or longitude) were predicted to 
have occurred outside the niche of the rockfish species. 
Thus, no rockfish were predicted to occur at stations 
outside of the species niche (i? = 0 in Eq. 1), whereas 
stations within the species depth, temperature, and 
longitudinal niches were predicted to have rockfish 
present (7? = !). For example, on average 90% of the 
juvenile Pacific ocean perch CPUE in the trawl hauls 
came from stations between 85 and 217 m depth, and 
therefore juvenile Pacific ocean perch were predicted 
to occur at stations within the depth range (R = l) and 
predicted not to occur at stations shallower and deeper 
than these depths (R = 0). The 5th and 95th percentiles 
of the cumulative distribution were used to reduce 
spurious data (such as where depth, temperature, or 
species identification were recorded incorrectly) and 
to reduce the effect of outlying catches that occurred 
at the extreme edges of the depth and temperature 
distributions of the species. There has been no indica- 
tion of changes in the underlying depth and tempera- 
ture niche dimensions of rockfish over time (NPFMC, 
2009). 
The second stage of the modeling was to develop a 
predictor of abundance for each rockfish species at sta- 
tions where they were predicted to be present. Up to six 
variables were used to model rockfish abundance: depth 
and temperature, as well as habitat variables chosen 
for their potential importance to growth and survival. 
The suite of habitat variables for each species included 
an index of local bottom slope (S); the ratio of the ther- 
mocline depth to the bottom depth (TD); an index of 
predation refuge based on coral and sponge abundance 
(CS); and for shrimp-eating species, an index of prey 
abundance (P) (Table 3). 
The index of local bottom slope was calculated for 
each trawl survey station by using bathymetry maps 
with depth contours in 100-ni increments from 0 to 
2000 m (derived from ETOP02 gridded elevation data, 
http://www.ngdc.noaa.gov/mgg/global/etopo2.html.). The 
bathymetry was kriged over the station grid for the 
Gulf of Alaska and the slope was calculated from this 
surface by using ArcGIS spatial analyst tools (ESRI, 
Redlands, CA). The local slope was extracted from this 
surface for a latitude and longitude pair at the midpoint 
of each bottom trawl haul. 
Productivity in the water column is often related to 
water column stratification (Whitney et ah, 2005; Strom 
et ah, 2007). For example, where the water column is 
well-mixed (where there is a small temperature differ- 
ence between surface and deeper water and a deep or 
absent thermocline), upwelling, wind, or tidal mixing 
may be occurring, indicating higher availability of nu- 
trients for primary productivity in the area. Conversely 
