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



187 



Seabirds 



Other fish 



Tuna 



Billfish 



Sharks 



i 

 -0.2 



SP Bluefin 



— « — Other seabirds (1 07) 

 -Albatross (0-99) 



Petrel (1.17) 



— Lancetfish(SN)(1) 

 "Opah(1 .03) 



Pomfret(1 16) 



-Lancetfish(LN)(1.02) 



Southern Ray's bream (0.96) 

 "© — Long finned bream (111) 

 ^Ray's bream (2.47) 



— © Sunfish (0.97) 



© Ribbonfish (0.93) 



Seabirds 



Other fish 



e Rudderfish (0.89) 



© Escolar (0.66) 



-e-Oilfish (0.98) 

 -e-Albacore (0.94) 



Slender tuna (0.9) 



"-Butterfly mackerel (0.93) 

 e Southern bluefin (1.4) 

 —©—Swordfish (0 9) 

 — Thintail thresher shark (0.88) 

 — Mako (0.93) 

 ©Blue shark (1 87) 

 -©"Porbeagle (0.92) 

 -© Ray (0.89) 



Tuna 



Billfish - 



Sharks 



— I — 

 0.0 



"I 



0.2 



SP Yellowfin 



© — ; Other seabirds (1.26) 



— © iBarracouta (0.99) 



— © — 'Slender barracuda (0.98) 



©-?— Opah (0.99) 



-©iancetfish (LN) (1 14) 



-s-Mahi mahi (1.09) 



— «r-Lancetfish (SN) (0.96) 



© Great barracuda (0.95) 



: -e- Ray , s bream (1.71) 

  — e — Sunfish (0.99) 

 ; — e— Oilfish (1.23) 



-©"Escolar (1.33) 

 -©"Skipjack (1 .06) 

 ©Yellowfin (2.33) 

 —f 3 — Southern bluefin (2.2) 

 ©Albacore(2.12) 

 -r 6 — Wahoo (0.96) 



-®-Bigeye(1 16) 

 — © — Sailfish (1 03) 

 — 9 — Blue marlin (0.88) 



r - e -Shortbill spearfish (0.99) 

 :— e — Black marlin (0.92) 

  -©-Striped marlin (0.94) 

 -©"Swordfish (0.85) 



©i Porbeagle (0.87) 



j-e Silky shark (0.86) 



Tiger shark (0.87) 



"Mako (1.06) 



-Ray (0 99) 



> — Bronze whaler (0.95) 



© — Oceanic whitetip (0.99) 



©-Blue shark (0.99) 



© Hammerhead (0.93) 



© Dusky shark (0.85) 



-0.2 



Soak time coefficient 



0.0 0.2 



Figure continued on next page. 



Figure 4 



Coefficients for the effect of soak time on the catch rates of the most abundant species in each fishery. The coefficients are from 

 random effects models where soak time is the only factor. Horizontal bars are 95% confidence intervals for the estimated coefficient. 

 The dispersion parameter is shown in parentheses (it is 1.00 for species that are distributed as predicted by the model, but may be 

 higher for species that have a more clumped distribution along the longline). 



mixed models (Wolfinger and O'Connell, 1993). To judge 

 the performance of the various model formulations, we 

 checked statistics, such as deviance and dispersion, and 

 examined scatter plots of chi-square residuals against the 

 linear predictor I rj) and QQ plots of chi-square residuals. 

 We used the AIC and BIC to compare the performance of 

 the various model formulations. 



Variance in the binomial model depends on only one pa- 

 rameter, P. A dispersion parameter is therefore necessary 

 to allow the variance in the data to be modeled. In effect, 

 the dispersion parameter scales the estimate of binomial 

 variance for the amount of variance in the data. The disper- 

 sion parameter will be near one when the variance in the 

 data matches that of the binomial model. Values greater 

 than one ("over-dispersion") imply that the species may 

 have a clumped distribution along the longline. 



Results 



Soak time 



For most species, soak time had a positive effect on catch 

 rates (Fig. 4). In addition to being statistically significant, 

 the effect of soak time made a large difference to catch 

 rates at opposite ends of the longline. In the South Pacific 

 yellowfin tuna fishery, for example, the expected catch rates 

 of swordfish can vary from 0.6 (CI ±0.1) per 1000 hooks 

 (5 hours) to 1.9 (CI ±0.3) per 1000 hooks (20 hours) 

 (Table 3). A soak time of 5 hours and 3500 hooks (if that 

 were possible) would result in a total catch of about 

 two swordfish. In contrast, almost seven swordfish are 

 expected from a longline operation of the same number of 

 hooks with 20 hours of soak time. 



