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Figure 3 
Diagram depicting the components of the simulation model. All permutations 
were modeled, except sea turtles placed according to a uniform probability 
distribution and fishing sets clumping in the same areas where turtles clump. 
Performance of estimation methods was evaluated by comparing the median 
relative error of bycatch estimates across estimation methods. 
mation method is unbiased, the median relative error 
should be zero. The precision of an estimation method 
can be measured by examining the interquartile range 
(IQR) of its relative errors. If the IQR of an estimation 
method is small, then that estimation method is precise. 
In addition to evaluating estimation methods using 
point estimates, we also examined confidence intervals 
(CIs). After we determined whether the delta-lognormal 
method or GLMs generated better point estimates, we 
eliminated the less suitable method from further con- 
sideration. Then we analyzed the effects of data pooling 
on the CIs of the more suitable estimation method. We 
calculated 95% CIs for each of the 1000 simulations 
under every spatial scenario and data pooling method. 
We examined the number of times the simulated total 
bycatch fell outside the 95% Cl for that simulation. We 
also considered the median Cl width for pooling meth- 
ods under each spatial scenario. 
Essentially, we generated 1000 estimates, as the SEF- 
SC would, for each estimation method in each spatial 
scenario. We made point estimates with each estimation 
method for each of the 1000 simulations under every 
spatial scenario. We calculated 95% CIs for the more 
suitable estimation method in each of the 1000 simula- 
tions under every spatial scenario. Because we knew the 
total amount of bycatch in each of the 1000 simulations, 
we were able to compare the bycatch estimates to the 
total amount of bycatch simulated and thus evaluate 
the performance of the estimation method. 
Results 
Method suitability based on point estimates 
We found the delta-lognormal method with stratum- 
level estimation to be the most accurate of the methods 
evaluated (Fig. 4). In the co-occurrence clumping sce- 
nario {Turtles , elump , Sefs clump . turtles ) and sets-only clump- 
ing scenario (Turtles unjform , Sets clump . sets ), stratum-level 
estimates were slightly more accurate than pooled esti- 
mates, and, in the remaining 3 spatial scenarios, no 
substantial difference was seen in accuracy between 
estimates at the stratum-level and estimates from all 
sets pooled. For each of the 5 spatial scenarios, there was 
also no substantial difference in the precision between 
delta-lognormal estimates at the stratum-level and delta- 
lognormal estimates from all sets pooled. 
The GLMs never outperformed the delta-lognormal 
methods. The GLMs had an accuracy similar to that 
of the delta-lognormal methods in the fully uniform 
scenario ( Turtles uni{orm , Sets um{orm ), and no substantial 
