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Fishery Bulletin 1 10(3) 
Co-occurrence clumping scenario In spatial scenarios 
with clumped fishing sets, we modeled computational 
groups with 5 clumps of 5 sets each. Sea turtles also 
were aggregated in 5 clumps for clumping scenarios. 
Each clump was based around a block of 9x9 cells. This 
use of clumps of 90x90 km was consistent with the 
results of Gardner et al. (2008), who reported that turtle 
bycatch distributions were found to span 30-200 km. 
We modeled the density of sea turtles as declin- 
ing with distance from the center of a clump. We se- 
lected x and y coordinates for the seed of the first 
turtle clump with uniform probability. To accentuate 
clumping, we placed turtles within a clump so that 
the coordinates closer to the seed had a greater prob- 
ability: Prob(X=X seed ) = 0.2, Prob(X=X seed±1 ) = 0.16, 
Prob(X=X seed±2 ) = 0.12, Prob(Z=X seed±3 ) = 0.08, 
Prob(X=A seed±4 ) = 0.04. Assuming a density of 0.5 tur- 
tles/km 2 , we placed an average of 50 turtles/cell or 4050 
turtles/clump and 20,250 turtles in the entire grid of 
100x100 cells. Subsequent clump seed coordinates were 
selected so that a set could not fish in multiple turtle 
clumps. 
In the spatial scenario with fishing sets and sea 
turtles clumped in the same areas, the co-occurrence 
clumping scenario, the clumps (9x9 cells) for the sets 
and clumps (9x9 cells) for the turtles were identical. 
Each fishing set began within the 9x9 cells of its clump 
and then moved 4 cells up, right, down, or left. A set 
could leave the 9x9 cells of its clump during fishing. 
However, clumps were designed with 9x9 cells so that 
a fishing set that began in a clump’s center could move 
in any direction and remain inside its clump. For each 
of the 5 fishing sets in a clump, the direction of fishing 
(up, right, down, or left) was determined by the number 
of turtles that would be encountered in each direction. 
To determine the initial coordinates of fishing sets, 
we tallied the number of sea turtles in each x coordi- 
nate of the clump. This tally was used to construct a 
probability for set placement by dividing the number 
of turtles with a particular .r coordinate by the total 
number of turtles. The same was done for the y coordi- 
nates. To determine the direction of fishing, we tallied 
the number of turtles that would be encountered by a 
set moving right, left, up, or down. These 4 counts were 
summed, and the number encountered in each direction 
was divided by the total to obtain a probability of mov- 
ing in each direction. The more turtles that would be 
encountered, the greater the probability a set would fish 
in that direction. This algorithm mimicked a situation 
where more turtles are in the productive areas that 
fishermen are targeting than in other areas. 
Independent clumping scenario The 2 features that 
distinguish the independent clumping scenario from 
the co-occurrence clumping scenario are the following: 
1) the clumps (9x9 cells) for fishing sets and turtles 
were placed independently and 2) the direction of fish- 
ing was influenced by the number of sets in each of the 
4 directions. That is, there was a positive relationship 
between the probability a set would fish in a particular 
direction and the proximity to other sets in that direc- 
tion. The smaller the distance to other sets, the greater 
the probability the set would fish in that direction. This 
algorithm is consistent with fishermen aggregating 
because of peer influence. 
Initial x and y coordinates were selected for the seed 
of the first fishing set clump with uniform probability. 
We also selected x and y coordinates for the starting 
positions of each of the 5 sets in a clump with uniform 
probability. Each set had a greater probability of mov- 
ing in the direction where there were more sets. We 
first considered Set Q Cell 0 , the first cell in the first set. 
We calculated the distances from Set 0 Cell 0 to Set t 
Cell 0 , where i = 1 to 4, and summed these distances. 
We calculated the distances from Set 0 Cell 1R , the cell 
to the right of the initial fishing cell, to Setfiell 0 and 
added these distances to the distances from Set 0 Cell 0 
to Set t Cell 0 . We continued to calculate the distances 
to Set t Cell 0 if Set 0 fished to the right and summed the 
distances. This algorithm gave the distance from Set 0 
to Set i Cell 0 if Set 0 moved right. We also calculated 
distances for Set 0 fishing up, left, and down. These 
calculations gave us 4 distances for Set 0 , one each for 
moving right, left, up, and down. The direction with 
the smallest distance between sets should have the 
greatest probability, so we divided each of the 4 dis- 
tances by the smallest distance. Next, we normalized 
the transformed distances to obtain a probability of 
Set 0 moving in each direction. We computed these prob- 
abilities for each of the sets to determine the direc- 
tion of fishing. Subsequent set clumps were placed to 
prevent the overlapping of sets from different clumps 
( Seed+\1< x or y <Seed-Yl). No contraints were placed 
upon the overlap of set and turtle clumps, and turtles 
were distributed as they were in the co-occurrence 
clumping scenario. 
Sets-only clumping scenario In the sets-only clump- 
ing scenario, when fishing sets were clumped but sea 
turtles were uniformly random, the direction of fishing 
was determined as it was in the independent clump- 
ing scenario. When turtles had a uniformly random 
distribution, they could occur in any cell in the grid of 
100x100 cells. To maintain consistency, we placed the 
same number of turtles across the entire grid in the 
uniformly random scenarios as we did in the 5 clumps 
in the clumping scenarios. Distributing 20,250 turtles 
across the grid with uniform probability resulted in an 
average of 2.025 turtles/cell and 0.0203 turtles/km 2 . 
Although turtle densities differed between uniformly 
random and clumping scenarios, different probabilities 
of capture were applied in the clumping and uniformly 
random scenarios to account for higher densities in 
clumping scenarios. The probabilities of capture are 
discussed below. 
Scenarios with uniformly random sets Fishing sets were 
uniformly random in 2 spatial scenarios: turtles-only 
clumping and fully uniform. In these spatial scenarios, 
set placement and direction of fishing were determined 
