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Fishery Bulletin 115(1) 
the stock of spiny dogfish was not subjected to the in- 
tense exploitation by the domestic commercial fishery 
that began in the early 1990s. This exploitation caused 
this stock to be declared overfished in 1998 and result- 
ed in the development of a fishery management plan 
in 1999 for this species in federal waters (5-322 km 
offshore) by the New England and Mid-Atlantic Fish- 
ery Management Councils (MAFMC^). The federal plan 
was further reinforced by an interstate fishery manage- 
ment plan for state waters (0-5 km offshore) developed 
by the Atlantic States Marine Fisheries Commission in 
2002 (ASMFC3; Dell’Apa et ah, 2015). 
Additionally, the commercial fishery has preferen- 
tially targeted adult females because of their larger 
size. This fishing strategy has resulted in a recent in- 
crease in the adult male:female sex ratio in the catch 
of this species and in a decrease in the average size at 
maturity for adult females in the U.S. Atlantic stock 
from the sizes reported by Nammack et al. (1985) (Sos- 
ebee, 2005; Rago and Sosebee”*) — a drop from 80 cm TL 
to about 74.5 cm TL, according to Bubley et al. (2013). 
Overall, this decline indicates that the actual size at 
maturity of adult females from the NEAMAP surveys 
during 2007-2013 was likely to have been smaller than 
the 80 cm TL used in our analysis. However, because 
no interannual variability in the predicted CPUE of 
adult females was found by using the 80-cm-TL size at 
maturity criterion and because of the inherent difficul- 
ties in choosing an alternative size criterion as a result 
of consistent fiuctuation in the annual average size at 
maturity reported for adult females (Marques da Silva 
and Ross®), we opted to adopt a more conservative ap- 
proach by using the most commonly accepted size at 
maturity reported by Nammack et al. (1985). 
For statistical purposes, the time of each set was 
classified into 3 categories: morning (between 6:00 AM 
and 12:59 PM), afternoon (between 1:00 PM and 6:59 
PM), and night (between 7:00 PM and 5:59 AM), ac- 
cording to the time partitioning used by DelFApa et 
al. (2014). 
Six environmental variables were included in the 
analysis: bathymetry (mean depth of each haul in feet 
and converted to meters for analysis), distance to shore 
(measured in meters), slope of the seabed (percent 
grade), monthly mean SST (measured in degrees Cel- 
^ MAFMC (Mid-Atlantic Fishery Management Council). 
1999. Spiny dogfish fishery management plan, 292 p. Mid- 
Atlantic Fishery Management Council, Dover, DE. [Avail- 
able from website.] 
^ ASMFC (Atlantic States Marine Fisheries Commis- 
sion). 2002. Interstate fishery management plan for spiny 
dogfish. ASMFC, Fish. Manage. Rep. 40, 98 p. [Available 
from website.] 
Rago, R, and K. Sosebee. 2012. Update on the status of 
spiny dogfish in 2012 and initial evaluation of harvest at 
the Fmsy proxy, 43 p. Science and Statistical Committee, 
Mid-Atlantic Fishery Management Council, Dover, DE. 
® Marques da Silva, H., and M. R. Ross. 1993. Reproduc- 
tive strategies of spiny dogfish, Squalus acanthias, in the 
NW Atlantic. ICES Council Meeting (C.M.) Documents 1993/ 
G:51,18 p. [Available from website.] 
sius), monthly mean chl-a concentration (measured in 
milligrams per cubic meter), and monthly mean values 
of practical salinity. 
Data for 3 variables — SST, chl-a concentration, and 
salinity — were extracted from the NASA Earth Obser- 
vations website (website) as long-term monthly mean 
climate data. Bathymetry was derived from the same 
NASA Earth Observations website by using the Gen- 
eral Bathymetric Chart of the Oceans (GEBCO) grid 
(website). In addition, data for bathymetry were col- 
lected at each haul location. These data were used to 
correct and check the information on final mean depth. 
When a discrepancy occurred between the GEBCO and 
survey data, a mean of the values in the 2 data sets 
was used. 
Distances to the coast and slope gradients were 
derived from the bathymetry map created with the 
GEBCO grid, by using the Near (World Equidistant Cy- 
lindrical coordinate system) and Slope Spatial Analyst 
tools in ArcGis 9.2® (Esri, Redlands, CA). 
All the covariates were aggregated at a resolution 
of 0.25°x0.25° and were transformed into raster lay- 
ers with the raster package (Hijmans, 2013) in R, vers. 
3.1.2 (R Core Team, 2014). To check collinearity be- 
tween explanatory environmental variables, a drafts- 
man’s plot and the Pearson’s correlation index were 
used. Because variables were not correlated highly 
with coefficients of correlation (r) <0.5, they were con- 
sidered in further analyses. 
Modeling species abundance 
For the purpose of our analysis and modeling, we used 
the spatial distribution approach, which combines ob- 
servations of species occurrence or abundance with en- 
vironmental estimates to predict spiny dogfish distribu- 
tion at locations that were not sampled (Austin, 2007; 
Elith and Leathwick, 2009). Different approaches and 
methods can be used to model the spatial distribution 
of a species. However, most of the common applications 
do not always provide accurate results when run with 
traditional prediction methods (i.e., frequentist infer- 
ence), often because of a large amount of spatiotempo- 
ral variability in the data that characterizes dynamic 
marine ecosystems (Roos et al., 2015). To account for 
this variability, we used hierarchical Bayesian spatio- 
temporal models in our study. 
Bayesian approaches have several advantages over 
traditional methods and have been applied successfully 
to fisheries studies (Colloca et al., 2009; Munoz et al., 
2013; Pennine et al., 2014). Bayesian methods allow 
the inclusion of both the observed data and model pa- 
rameters as random variables (Banerjee et al., 2004) 
and provide more realistic and accurate estimations of 
uncertainty (Pennine et al., 2014). Additionally, they al- 
low the use of spatial and temporal components as a 
® Mention of trade names or commercial companies is for iden- 
tification purposes only and does not imply endorsement by 
the National Marine Fisheries Service, NOAA. 
