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Fishery Bulletin 1 10(3) 
Table 3 
Median widths of confidence intervals (CIs) from the 5 spatial scenarios and 2 spatiotemporal scales of delta-lognormal esti- 
mation in our simulation model of interactions of sea turtles with the U.S. Atlantic pelagic longline fishery. The numbers in 
parentheses represent the median widths of the CIs as percentages of the bycatch point estimates. The co-occurrence clumping 
scenario and sets-only clumping scenario were considered the most realistic spatial scenarios. 
Spatiotemporal scale for estimation 
Spatial scenario 
Stratum level 
All sets pooled 
Co-occurrence clumping (Turtles , clump , Se£s clump . turtles ) 
Independent clumping ( Turtles clump , Se£s clump . sets ) 
Sets-only clumping (Turtles unlform , Sefs clump _ sets ) 
Turtles-only clumping ( Turtles dump , Sets uniform ) 
Fully uniform distribution (Turtles ani{orm , Sets uni(orm ) 
649.8 (84.1%) 
100.4 (315.2%) 
570.6 (92.5%) 
84.4 (402.6%) 
523.3 (89.8%) 
402.8 (53.4%) 
88.5 (268.7%) 
355.7 (59.3%) 
74.0 (322.7%) 
335.2 (58.0%) 
difference was seen between GLM-P and GLM-NB per- 
formance (Fig. 4). However, the GLMs produced more 
outliers than the delta-lognormal methods in the fully 
uniform scenario (Turtles uni{orm , Sets uni{orm ). The GLMs 
were biased lower than the delta-lognormal methods 
in the co-occurrence clumping scenario (Turtles clump , 
5c^s c | u mp. turtles^ and sets-only clumping scenario ( Turtle - 
^uniform ’ S ^ s ciump-set s ); The GLM-P was less biased and 
more precise than the GLM-NB in the co-occurrence 
clumping scenario (Turtles t clump , Seis clump . turtles ). 
The delta-lognormal method with stratum-level es- 
timation and the delta-lognormal method for all sets 
pooled performed equally well in the independent 
clumping scenario (Turtles ciump , Se£s clump _ sets ) and tur- 
tles-only clumping scenario (Turtles clump , Sets um(orm ). 
However, in these spatial scenarios, the simulated by- 
catch rates on sets with observers were much lower 
than the rates reported to the SEFSC by observers. 
Although the mean bycatch rate from SEFSC observer 
data was 0.062 turtles/set (minimum 0.031 turtles/set, 
maximum 0.081 turtles/set), the mean bycatch rate 
from simulated observers was 0.006 turtles/set in the 
independent clumping scenario (Turtles clump , Se£s c | ump 
sets ) and 0.004 turtles/set in the turtles-only clumping 
scenario (Turtles clump , Se£s uniform ). By comparison, the 
mean bycatch rate from simulated observers was 0.122 
turtles/set in the co-occurrence clumping scenario (Tur- 
ffeSciump ’ S ^ s ciump-turties)- 0 098 turtles/set in the sets- 
only clumping scenario (Turtles u „ iform , Se£s clump . sets ), and 
0.095 turtles/set in the fully uniform scenario (Turtle- 
s umform’ ^^uniform^' There were also more outliers in the 
independent clumping scenario (Turtles clump , >Se£s clump . 
sets ) and turtles-only clumping scenario (Turtles clu , 
Sets uni f orm ), and the IQRs and whiskers (data within 1.5 
times the IQR) were larger for these 2 spatial scenarios 
than for the other 3 scenarios. 
Convergence problems in GLMs 
The GLM-P and GLM-NB did not converge for stratum- 
level estimation in any spatial scenario. For example, 
in the co-occurrence clumping scenario (Turtles clump , 
^^dump-turtles)’ the spatial scenario with the greatest 
mean observed bycatch rate, the median number of 
strata with observed take was 19 out of 32. For strata 
with observed take, the median number of sets with take 
was 2. The stratum-level GLMs could not converge with 
such small sample sizes. 
Therefore, the GLM-P and GLM-NB methods were 
considered for estimation only with all sets pooled. Fur- 
ther, for a reason similar to that for the failure of the 
GLMs at the stratum-level, the GLM-P and GLM-NB 
methods for estimation with all sets pooled did not 
converge in the independent clumping scenario (Turtles- 
ciump’ S ^ s c!ump-set s ) or turtles-only clumping scenario 
(Turtles clump , Sets uni(orm ). The average number of ob- 
served sets with take, out of all observed sets pooled, 
was 2.64 for the independent clumping scenario ( Turtles - 
clump’ Se * s ciump-sets) and L86 for the turtles-only clump- 
ing scenario (Turtles clump , Se£s uniform ). Therefore, GLM 
results are not presented for these 2 spatial scenarios. 
Confidence intervals 
In addition to generating an accurate point estimate, a 
bycatch estimation method should be able to produce a 
suitable measure of uncertainty, such as a CI. For every 
spatial scenario, the median 95% CI calculated from the 
delta-lognormal method was narrower with estimation 
from all sets pooled than with estimation from strata 
(Table 3). In the 2 spatial scenarios thought to be most 
realistic, the co-occurrence clumping scenario (Turtles- 
ciump’ Se ^ciump-turtie s ) and sets-only clumping scenario 
(Turtle Suniform’ Sefs clump _ 8et8 ), the median widths of the 
CIs based on all sets pooled were -54% and -59% of 
the point estimates, respectively. However, the median 
widths of the CIs from stratum estimates were -84% 
and -93% of the point estimates, respectively (Table 3). 
Although the median CIs from all sets pooled were 
narrower, instances of the total simulated bycatch fall- 
ing outside the CI occurred more often with all sets 
pooled than at the stratum level (Table 4). With 95% 
