Ward et al.: The effect of soak time on pelagic longline catches 



189 



Tuna 



NP Swordfish 



h Blgeye (0 94) 



3 Pacific bluefin (0.95) 



CP Bigeye 



Billfish 



Sharks 



Tuna 



*— Skipjack (0-84) 

 -s-Yellowfin (0 87) 

 ^Albacore (1.03) 

 Slue marlin (0.91) 

 — Shortbill spearfish (0.93) 

 "Swordfish (0 96) 

 -°-Striped marlin (0.88) 

 e Sailfish (0 96) 



Billfish 



-Salmon shark (0.96) 



"° — Oceanic whitetip (0.98) 



" e Crocodile shark (0 89) 



- e — Short finned mako (0 95) 

 e Blue shark (0.96) 

 e Bigeye thresher shark (0.83) 



Sharks 



-0.2 



0.0 



— I 

 0.2 



-0.2 



Soak time coefficient 



Figure 4 (continued) 



^Skipjack (0.85) 

 e Albacore (0.81) 

 e Yellowfin (0.93) 

 bigeye (0.88) 



Black marlin (0.89) 



e Stnped marlin (0 86) 

 ■^Shortbill spearfish (0 89) 

 "^Blue marlin (0.86) 



— e Sailfish (1.05) 



"^Swordfish (0.9) 



— Sandbar shark (1.24) 



e Bignose shark (1.19) 



_e— Short finned mako (0.94) 



e Blue shark (0 81) 

 -e-Silky shark (0.93) 

 -° — Pelagic thresher shark (0.88) 



"^Oceanic whitetip (1.01) 

 "^Bigeye thresher shark (0.86) 



e Long finned mako (0.86) 



e Thintail thresher shark (0.9) 



0.0 



"Dusky shark (1.05) 

 -°— Crocodile shark (0.89) 

 I 

 0.2 



in 36 of the 48 models for the other six species. We con- 

 cluded that the fixed effects modified the intercept of the 

 soak-time-catch-rate relationship, but they rarely altered 

 the slope of the relationship. 



Akaike's information criterion (AIC) and Sawa's Bayes- 

 ian information criterion (BIC ) both indicated that models 

 with soak time as the only variable were the most or second 

 most parsimonious model. This was the case for all models, 

 except for several models of albacore and long-nosed lan- 

 cetfish. Therefore the following discussion concentrates on 

 the effects of soak time and timing on catch rates. 



Discussion 



In considering results of the random effects models, we 

 examined patterns in the effects of soak time and timing 

 among taxonomic groups, the mechanisms that may cause 

 the patterns, and their implications. First, however, we 

 investigated whether the effects were consistent for the 

 same species between fisheries. 



Comparison of fisheries 



The effect of soak time was consistent for several spe- 

 cies between the fisheries, despite significant differences 

 in fishing practices and area and season of activity. For 

 example, the soak time coefficients for species in the South 

 Pacific yellowfin tuna fishery were very similar to those of 

 the same species in the Central Pacific bigeye tuna fishery 

 (r=0.79) (Fig. 6). 



Several species had a narrow range of soak time coef- 

 ficients over all the fisheries analyzed. Estimates of the 

 coefficient of yellowfin tuna, for example, ranged from 0.00 

 (CI ±0.01) in the South Pacific yellowfin fishery to 0.04 

 ( CI ±0.0 1 ) in the North Pacific swordfish fishery. A coefficient 

 of 0.04 is equivalent to a difference of 1.3 yellowfin tuna per 

 1000 hooks between longline segments with soak times of 

 5 and 20 hours. The range in coefficients is also small for 

 other abundant and widely distributed species, such as al- 

 bacore (r=0. 00-0.05) and blue shark (r=0.01-0.05). 



For many species, however, the correlation between soak- 

 time coefficients from different fisheries was poor (Fig. 6). 



