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Figure 4 
Results of the normed principal components analysis (PCA) on (A) small- and (B) large-scale environmental data 
from CTD casts conducted during the bottom-longline survey in the northern Gulf of Mexico during 2006-09, trawl 
data from the Southeast Area Monitoring and Assessment Program database (http://seamap.gsmfc.org) for 2007- 
2009, and from the moderate resolution imaging spectroradiometer on the Aqua satellite (http://modis.gsfc.nasa.gov) 
for 2006-2009. Numbers within the panel correspond to the sampling blocks (1-8) and statistical zones (4-21, minus 
12) of the small- and large-scale surveys, respectively, used in our analysis (blocks and zones are defined in Figures 
1 and 2). Arrows represent abiotic variables, and dashed-line circles represent correlation circles with a unit of 1. 
Variation explained by the first (PCI) and second (PC2) principal components is 74.18% for the small-scale survey 
and 65.88% for the large-scale survey. The scale is shown in ovals at top of each panel. Cbio=crustacean biomass, 
Chl-a=chlorophyll-a, DO=dissolved oxygen, Fbio=fish biomass, Sal=salinity, Temp=temperature. 
The centered PCA conducted with large-scale data 
on shark abundance explained 87.30% of the vari- 
ability between observations (across NMFS statistical 
zones) on the first 2 principal components (Fig. 3B). 
Variation along PCI was mainly explained by data for 
Atlantic Sharpnose Shark, which was more abundant 
in zones 11, 14, and 16 (western zones), and variation 
along PC2 was mainly explained by data for Blacknose 
Shark, which was more abundant in zones 3 and 5 
(eastern zones) (Fig. 3B). Compared with other species, 
Bull Shark displayed a weaker pattern because of their 
lower abundances (Fig. 3B). 
The normed PCA on large-scale environmental data 
explained 65.88% of the variability between observa- 
tions (NMFS statistical zones) on the first 2 principle 
components (Fig. 4B). Fish biomass and temperature 
were correlated, and both of these variables were 
negatively correlated with depth. These 3 variables 
explained most of the variability along PCI (Fig. 4B). 
Chl-a concentration and crustacean biomass were 
positively correlated, and concentration of chl-a had a 
strong negative correlation with dissolved oxygen. To- 
gether, these 3 variables explained the majority of vari- 
ability along PC2. NMFS statistical zones 11, 14, 15, 
and 16 were characterized by high chl-a concentration, 
and zones 18 and 19 were characterized by high fish 
biomass. Conversely, eastern zones 4-6 were character- 
ized by low fish and crustacean biomass (Fig. 4B). 
The COIA that coupled large-scale shark abundance 
and environmental data was characterized by a total in- 
ertia of 0.20 and a RV coefficient of 0.42, indicating good 
agreement between the 2 data sets. Axes 1 and 2 support- 
ed 97.32% of this common structure (Fig. 5B). Abundance 
of Atlantic Sharpnose Shark was strongly related to chl-a 
concentration and had a strong negative relation to dis- 
solved oxygen. Spinner Shark showed a similar pattern. 
Blacktip Shark abundance was related to crustacean 
biomass and had a strong negative relation to salinity. 
Abundance of Blacknose Shark was strongly related to 
temperature and inversely related to depth (Fig. 5B) 
Discussion 
For comparison of the factors that affect the distribu- 
tion of sharks across spatial scales, COIA provides a 
