358 
Fishery Bulletin 1 10(3) 
had a median relative error of -0.17 in the turtles-only 
clumping scenario {Turtles elump , Sets um{orm ). The me- 
dian relative error was only -0.05 in the co-occurrence 
clumping scenario {Turtles clump , Scts clump . turtles ) and 
-0.02 in the sets-only clumping scenario {Turtles unif - orm , 
Sets 
clump-sets 
), the 2 most realistic spatial scenarios. 
Confidence intervals 
CIs were narrower for estimates from all sets pooled 
than for stratum-level estimates because the variance 
in bycatch rates was larger when calculated for strata 
than when calculated for all sets pooled. Consideration 
of Cl width as a percentage of the bycatch point estimate 
highlighted how wide and, therefore, uninformative was 
the standard Cl based on strata. Narrowing the Cl with 
calculations from all sets pooled helped address this 
problem, but the problem of wide CIs was compounded 
by more underestimation than desired. For protected 
species conservation, underestimation is more prob- 
lematic than overestimation. It is important to know a 
lower bound estimate for protected resource conserva- 
tion because protected species have an incidental take 
limit that, if crossed, triggers formal consultation under 
section 7 of the ESA. 
Simulation model assumptions and their implications 
Although a simulation model never captures reality 
perfectly, it is important to consider the effects of model 
assumptions on results. We attempted to make reason- 
able assumptions both when incorporating well under- 
stood aspects of interactions of sea turtles and the U.S. 
Atlantic pelagic longline fishery and when modeling 
unknown features. However, each component of the 
simulation model could be designed in many ways. We 
consider the most influential assumptions to be: 1) spa- 
tial constraints, 2) the algorithm for selecting explana- 
tory variable values for the GLM, and 3) the simplified 
effort-based distribution of observers. 
First, density of sea turtles, the spatial configuration 
of sea turtles, the spatial characteristics of fishing, and 
their interactions had to be defined explicitly in our 
model. We made assumptions regarding the number of 
turtles, size of clumps, number of clumps, how turtles 
or fishing sets should be placed in clumps, how clumps 
could overlap, and the extent of the study area. Clump 
placement, the number of turtles per cell, the initial 
cell of a set, and the direction of fishing had stochastic 
elements, but model results could be influenced by con- 
straints on the dimensions and spatial distribution of 
turtles and sets. Further, we acknowledge that our 5 
spatial scenarios did not fully replicate reality. We at- 
tempted to represent a range of possible distributions, 
both to model longline interactions with sea turtles and 
to highlight properties of the estimation methods that 
could be relevant to other systems. Perhaps, the next 
step would be to combine multiple spatial scenarios in 
one model of fishery interactions. In other words, varia- 
tion in spatial distributions could be more realistically 
captured by including more than one spatial scenario 
in a simulation. 
Second, the GLM is based on the premise that envi- 
ronmental or fishing conditions can be used to predict 
the number of sea turtles caught. Therefore, the man- 
ner in which explanatory variable values were assigned 
to fishing sets in the simulation could have affected 
GLM performance. We selected variable values from 
sets observed by the SEFSC from 2005 to 2007 while 
attempting to account for the spatial distribution and 
stratum characteristics of the sets. However, variable 
values could be assigned in many ways, and different 
procedures could influence how well the GLMs esti- 
mated bycatch. Nevertheless, violation of GLM model 
assumptions could still be a problem even if a more 
realistic algorithm for selecting explanatory variable 
values was identified. Since some degree of set and 
turtle clumping seems to occur in nature and counts are 
at least dependent within a trip, violations of GLM-P 
assumptions are likely even with an improved algo- 
rithm for selecting explanatory variable values. Per- 
haps, GLM-NB performance would be improved under 
a more suitable algorithm for selecting explanatory 
variable values. However, the GLM-NB is typically used 
to address overdispersion (Welsh et ah, 1996; Thurston 
et ah, 2000; Lindsey, 2004; Venables and Dichmont, 
2004), and little overdispersion was detected in our 
simulation model. 
Overall, we do not expect the performance of the 
GLM-P to change in comparison with the delta-lognor- 
mal method. Also, the performance of the stratum-level 
delta-lognormal method compared with the performance 
of the delta-lognormal method with all sets pooled is 
likely robust. However, the GLM-NB could improve its 
performance relative to the other estimation methods if 
a clearer functional relationship between the explana- 
tory variables and the level of bycatch was captured. 
Third, we modeled a simplified effort-based distribu- 
tion of observers to simulate observer data for estimat- 
ing bycatch. If there are different patterns in SEFSC 
observer data and simulated observer data, the perfor- 
mance of the estimation method in the simulation may 
not accurately reflect the performance of the estimation 
method in the actual fishery. The SEFSC currently 
selects vessels for each calendar quarter and fishing 
area based on how many sets a vessel fished in that 
stratum in the previous year (Beerkircher et ah, 2004; 
Fairfield and Garrison, 2008). Vessels that fished more 
sets in the previous year have a greater chance of being 
observed by the SEFSC in the current year, and a ves- 
sel may be observed up to 4 times a year (Beerkircher 
et ah, 2004). Our simulation model, however, did not 
cover multiple years, and therefore the quarter-area 
effort data from the previous year were not available 
for the distribution of observers. Instead, we selected a 
cell at random to serve as an area of high effort and 
placed observers on the 2 sets (of the 25 simulated sets 
in a computational group) that were closest to that cell. 
The patterns that we were able to simulate, and that 
we believe are most relevant, are 8% observer coverage 
