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



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Observer data 



National authorities and regional organizations placed 

 independent observers on many longliners operating in the 

 six fisheries during the 1990s. The observer data consisted 

 of records of the species and the time when each animal 

 was brought on board. We restricted analyses to operations 

 where the last hook that had been deployed was retrieved 

 first ("counter- retrieved"), where there was no evidence of 

 stoppages due to line breaks or mechanical failure, and 

 where there was continuous monitoring by an observer. 

 Combined with records of the number of hooks deployed 

 and start and finish times of deployment and retrieval, the 

 observer data allowed calculation of soak time and catch 

 rates of longline segments. We aggregated catches and the 

 number of hooks into hourly segments. The soak time was 

 estimated for the midpoint of each hourly segment. 



The Central Pacific bigeye tuna and North Pacific sword- 

 fish fisheries differed from the other four fisheries in the 

 species that were recorded and the method of recording 

 the time when each animal was brought on board. Observ- 

 ers reported catches according to a float identifier in the 

 Central and North Pacific fisheries. Therefore we estimated 

 soak times for each longline segment from the time when 

 each float was retrieved. For those fisheries, observers re- 

 ported the float identifier only for tuna, billfish, and shark 

 (Table 2). Data are available for protected species, such 

 as seals, turtles, and seabirds but were not sought for the 

 present study. 



We assumed a constant rate of longline retrieval 

 throughout each operation. The number of hooks retrieved 

 during each hourly segment was the total number of hooks 

 divided by the duration of monitoring (decimal hours). For 

 each species we analyzed only the operations where at least 

 one individual of that species was caught. 



Longline segments that involved a full hour of monitor- 

 ing had several hundred hooks. Segments at either end 

 of the longline involved less than an hour of monitoring 

 and had fewer hooks. Catch rates may become inflated in 

 segments with very small numbers of hooks. Therefore we 

 arbitrarily excluded segments where the observer moni- 

 tored less than 25 hooks. 



For four of the fisheries, data were available on survival 

 rates, allowing the investigation of the relationship be- 

 tween soak time and hooking mortality. For the Western 

 Pacific and South Pacific fisheries, observers reported 

 whether the animal was alive or dead when it was brought 

 on board. We calculated survival rate (the number alive 

 divided by the total number reported dead or alive) for spe- 

 cies where data were available on the life status of more 

 than ten individuals. 



Generalized linear mixed model 



Logit model We applied a generalized linear mixed 

 model to the observer data. The model is based on a logis- 

 tic regression, with the catch (y) on each hook assumed 

 to have a binomial distribution with y ~ b(ra, n). n is the 

 expected value of the distribution for a specified soak time. 

 We refer to it as the probability of catching an animal or 



