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bile species than Atlantic Sharpnose Sharks, and they 
are capable of moving hundreds of kilometers on short 
time scales, as illustrated by traditional (Kohler et al., 
1998) and pop-up satellite archival (senior author and 
S. Powers, unpubl. data) tagging data. In contrast, At- 
lantic Sharpnose Sharks have a relatively small home 
range (Carlson et al., 2008). Blacktip Sharks, compared 
with Atlantic Sharpnose Sharks, may show higher va- 
gility when faced with a patchy prey environment. For 
example, Atlantic Sharpnose Sharks sampled in the 
small-scale survey showed relative trophic plasticity. 
Portunid crabs and shrimp contribute more to the diet 
of Atlantic Sharpnose Sharks sampled west (blocks 1-4 
in the current study) than to the diet of this shark spe- 
cies east (blocks 5-8) of Mobile Bay, and therefore may 
reflect differences in the prey base between these 2 ar- 
eas (Drymon et al., 2012). These findings indicate that 
the Atlantic Sharpnose Shark may have a wider di- 
etary breadth than the Blacktip Shark and may, there- 
fore, be responding to gradients in overall production 
as opposed to fish or crustacean biomass, specifically. 
Distributions of Atlantic Sharpnose and Spinner 
Sharks at both large and small scales were negative- 
ly related to dissolved oxygen. This relationship has 
been previously identified for other species of juvenile 
sharks. In Chesapeake Bay, Virginia, tree-based regres- 
sion models indicated the importance of dissolved oxy- 
gen as a factor that influences the distribution of juve- 
nile Sandbar Shark ( Carcharhinus plumbeus) (Grubbs 
and Musick, 2007). Similarly, researchers have noted 
that, although dissolved oxygen is not as widely consid- 
ered as temperature or salinity, it may play an impor- 
tant role as an environmental influence that affects the 
distribution of top predators in coastal environments, 
as has been demonstrated for juvenile Bull Shark in 
Florida waters (Heithaus et al., 2009). 
In our study, a wide size range of Spinner Shark 
was documented across both the small- and large-scale 
surveys. On the basis of age and growth data (Carl- 
son and Baremore, 2005), the mean sizes of Spinner 
Shark captured in small- and large-scale surveys corre- 
sponded to the ages of approximately 1 and 4 years old, 
respectively. Conversely, across surveys at both spatial 
scales, the mean size of Atlantic Sharpnose Shark was 
indicative of mature, adult animals (Carlson and Bare- 
more, 2003). Our findings, therefore, support previous 
work that indicated the importance of dissolved oxygen 
as an influence on the distribution of juvenile sharks 
(Grubbs and Musick, 2007; Heithaus et al., 2009) and 
indicates that dissolved oxygen may influence the dis- 
tribution of adult sharks as well. 
Distributions of Blacknose Shark were best ex- 
plained by depth, the direction of which varied as a 
function of scale. On the small scale, Blacknose Shark 
distribution was strongly and positively associated 
with water depth (i.e., deeper water resulted in higher 
Blacknose Shark CPUE). Conversely, at the large scale, 
distribution of Blacknose Shark were strongly and neg- 
atively associated with deep water (i.e., the shallower 
the depth, the lower the observed CPUE Blacknose 
Shark). This apparent dichotomy highlights differenc- 
es in the range of depths associated with each spatial 
scale and likely reflects a preferred depth range for 
this species. Small-scale sampling occurred at depths 
up to ~20 m, and large-scale sampling occurred pri- 
marily at depths >20 m. Discrete depth preferences for 
Blacknose Shark have previously been documented. 
Analyzing the same 2 bottom-longline data sets used 
in our analyses, Drymon et al. (2010) showed a discrete 
depth preference of 10-30 m for Blacknose Shark. Our 
data support these findings yet provide no additional 
insight into why Blacknose Shark occupy these depths. 
Although our analyses identified factors that may 
influence the distribution of selected shark species at 
2 different spatial scales, our approach has certain 
limitations. For instance, the faunal component of our 
analyses was based on catch data (CPUE). Bait loss 
can affect CPUE calculations (Torres et al., 2006). In 
areas where (or during times when) bait loss is high, 
CPUE may be artificially low. Recording the status of 
individual gangions (i.e., fish caught, bait present, bait 
absent) allows for hook-specific CPUE to be calculated, 
resulting in more accurate determination of CPUE and, 
hence, improving the power of this approach. In addi- 
tion, the analyses we used are sensitive to the tem- 
poral alignment of the data sets used. Restriction of 
analyses to data collected with the same methods and 
during the same time period will facilitate the iden- 
tification of reliable relationships between faunal and 
explanatory data. 
Conclusions 
Identification of the factors that affect the distribution 
of large predators is challenging. Our analysis encom- 
passes physical parameters (salinity, temperature, dis- 
solved oxygen, and depth), proxies for primary (chl-a 
concentration) and secondary (trawl) productivity, and 
predatory data sets across 2 spatial scales. Our results 
indicate that the factors that affect the distribution of 
sharks in the Gulf of Mexico are species dependent but 
may transcend the spatial boundaries that we exam- 
ined. As physical and biological characteristics of eco- 
systems in the Gulf of Mexico change, species-specific 
knowledge of how these factors influence the distribu- 
tions of top predators will be critical for the implemen- 
tation of proactive management measures. 
Acknowledgments 
The authors wish to thank all members of the Fisher- 
ies Ecology Laboratory at Dauphin Island Sea Labora- 
tory (DISL), as well as members of the NOAA South- 
east Fisheries Science Center Mississippi Laboratories 
shark team for the countless hours they spent at sea 
collecting valuable data. Data from DISL’s Fisheries 
