346 
Fishery Bulletin 1 10(3) 
bycatch has been estimated since 1992. McCracken 
(2004) determined that the GLM with a Poisson error 
distribution and its generalized additive model (GAM) 
counterpart were the most appropriate methods for es- 
timating sea turtle bycatch in the U.S. Pacific pelagic 
longline fishery from 1994 to 1999. However, McCracken 
did not consider the delta-lognormal method, and data 
from the Atlantic fishery were not analyzed. The Pacific 
fishery was closed in 2000 and reopened in 2004. Since 
then, observer coverage has been at least 20%, and by- 
catch has declined to the point that it is not necessary 
to model bycatch; instead, the Horvitz-Thompson esti- 
mator has been used by the PIFSC (McCracken, 2004). 
The goal of this study was to evaluate delta-lognormal 
and GLM performance under a variety of spatial fish- 
ery scenarios to identify the more suitable estimation 
method. We built a simulation model representing a 
range of spatial interactions of sea turtles with the U.S. 
Atlantic pelagic longline fishery and used the delta- 
lognormal method, a generalized linear model with a 
Poisson error distribution (GLM-P), and a GLM with 
a negative binomial error distribution (GLM-NB), each 
at 2 spatiotemporal scales, to estimate the number of 
turtles caught. By comparing these estimates to the 
total number of turtles caught in the simulation, we 
were able to systematically evaluate the performance 
of each method. 
Materials and methods 
To represent sea turtle bycatch by the U.S. Atlantic 
pelagic longline fishery, we constructed a simulation 
model that included 5 spatial scenarios with various 
distributions of sea turtles and fishing sets (Fig. 1). 
The simulation model included both SEFSC data and 
model assumptions based on the current understanding 
of fishery and sea turtle behavior (Table 1). Observers 
were simulated on 8% of the fishing sets, and each esti- 
mation method was applied to every spatial scenario. 
The estimation methods were evaluated by comparing 
the estimated amount of bycatch to the total simulated 
amount of bycatch. The simulation model was run 1000 
times for each of the 5 spatial scenarios, enabling a 
comprehensive evaluation of the performance of the 
estimation methods. 
The empirical and theoretical foundation 
of model assumptions 
Fishery-independent data on sea turtle spatial distribu- 
tions are limited to a few satellite-tracked individuals, 
at most 60 turtles in a study but typically fewer than 20 
(Godley et ah, 2007), and aerial surveys (Epperly et ah, 
1995; McClellan, 1996; McDaniel et ah, 2000; Goodman 
et ah, 2007). Small sample size, short study durations 
(typically less than one year), and nonrepresentative 
sampling of ages and sexes make satellite tracking data 
unsuitable for our study (Godley et ah, 2007). Moreover, 
inference from aerial surveys can be difficult because 
of the high percentage of time that turtles spend sub- 
merged and variability in turtle surfacing behavior 
related to season and location (Byles, 1988; Nelson, 
1996; Mansfield, 2006; Goodman et ah, 2007). 
Because fishery-independent data were not suitable 
for our objectives, we considered fishery-dependent 
data. These data indicate that sea 
turtles clump (i.e., tend to concen- 
trate in certain areas rather than 
occur equally spaced or spaced with 
uniform probability), especially in 
productive areas of the ocean. Cur- 
rents, frontal regions, and some 
bathymetric features often are as- 
sociated with enhanced produc- 
tivity and prey aggregation, and 
turtles exhibit a clumping pattern 
in response to these features when 
they forage (Williams et ah, 1996; 
Witzell, 1999; Gilman et ah, 2006). 
Environmental features, such as 
major current systems and gradi- 
ents in temperature, chlorophyll, 
and salinity, also seem to influence 
the clumping of turtles, as well as 
swordfish (Bigelow et ah, 1999; Po- 
lovina et ah, 2000; Lewison et ah, 
2004). However, turtle distributions 
appear to vary seasonally and be- 
tween species. Gardner et ah (2008) 
found that, for most of the year, log- 
gerhead and leatherback bycatch lo- 
cations were not completely random 
Co-occurrence clumping 
^^clump-turtles ) 
Independent clumping 
( 7u/t/es clump , Sefs clump . s8ts ) 
Sets-only clumping 
(Tunies u „ tm „, Sefs c , ump . se „) 
■ _ 
*v 
■ ■ ★★ 
■ ★ ★ 
■ ■ 
■ 
* * * 
■ 
*★ . ★ ** . 
* ■ ■ ■ ■ 
"A’ 
* . 
" ■ 
★ 
Turtles-only clumping 
Fully uniform distribution 
(rurt/es c i„ mp . Se/s unjform ) 
( Turtle s. 
■ ■ 
■ i 
*★ 
★ ★ * _ 
★ 
+ ■ 
. *** 
■ 
■ 
★ 
. '^r=turtle 
^ ®=fishing set 
Figure 1 
The 5 spatial scenarios depicting interactions between the U.S. Atlantic 
pelagic longline fishery and sea turtles that were included in the simulation 
model used in our study. The panels proceed left to right from the scenario 
considered most realistic at the top left to the least realistic at the bottom 
middle. +=turtle. ■ = fishing set. 
