Woodworth-Jefcoats et al.: Oceanographic variability, fishery expansion, and longline catches in the North Pacific 
229 
dolphinfish (Coryphaena hippurus), also known as mahi 
mahi; yellowfin tuna ( Thunnus albacares ); striped mar¬ 
lin ( Kajikia audax); sickle pomfret ( Taractichthys stein- 
dachneri); and opah ( Lampris guttatus). This fishery 
also catches but discards several noncommercial spe¬ 
cies, such as the longnose lancetfish ( Alepisaurus ferox) 
and snake mackerel (Gempylus serpens ). Recent stud¬ 
ies have noted increased catch rates of these noncom¬ 
mercial species concurrent with declines in the catch 
rate for target species. These changes have been at¬ 
tributed to increasing fishing effort (Ward and Myers, 
2005b; Polovina et ah, 2009) and prey release of the 
often smaller, noncommercial fish as larger target spe¬ 
cies are removed (Polovina and Woodworth-Jefcoats, 
2013). These studies support the previous finding that 
longline fisheries function as a keystone predator in 
the central North Pacific Ocean (Kitchell et ah, 2002). 
Despite spanning millions of square kilometers, pe¬ 
lagic fisheries have often been examined as a spatial 
aggregate (e.g., Cox et ah, 2002; Kitchell et ah, 2002; 
Sibert et ah, 2006). Previous studies of the Hawaii- 
based longline fishery, for example, have used spatially 
averaged trends focused on the core region of the fish¬ 
ery’s operating area (12-27°N; Polovina et ah, 2009; 
Polovina and Woodworth-Jefcoats, 2013). Shifting spa¬ 
tial patterns in fishing effort and the influence these 
changes may have on catch in the central North Pacific 
Ocean are under-explored in the primary literature (al¬ 
though see Gilman et ah, 2012; Walsh and Brodziak, 
2015). Additionally, the effect that international com¬ 
petition has had on the movement of the Hawaii-based 
fleet has not been explored. In this study, we aimed 
to determine how both the changing spatial footprint 
of the fishery and oceanographic variability have in¬ 
fluenced catch magnitude and composition, the under¬ 
standing of which is essential for ensuring a sustain¬ 
able and cost-effective fishery. 
Materials and method 
Materials 
We used both logbook and observer records in this 
study. Logbook data are reported by fishing vessel mas¬ 
ters and contain records of all hooks set (time, date, 
and location), as well as all commercially valuable 
catch. Observer data cover an average of roughly 17% 
of the fishing effort in the study period and contain 
records of all hooks set (time, date, and location), as 
well as all catch, regardless of commercial value. The 
distribution of observer data correlates well with that 
of the logbook data (Suppl. Fig. 1) (online only), and taken 
together the 2 data sets provide a robust measure of 
both fishing effort and catch from 1995 through 2015. 
Logbook data are complete through 2015 and observer 
data through 2014. We used all deep-set fishery data, 
which span the area of 16°S-42°N and 179-120°W. We 
defined deep sets as those with >10 hooks/float (Po¬ 
lovina et al., 2009; Polovina and Woodworth-Jefcoats, 
2013). Logbook data are collected by the Pacific Islands 
Fisheries Science Center. Observer data are collected 
by the Pacific Islands Regional Office. 
We used publicly available data for longline effort 
from the Western and Central Pacific Fisheries Com¬ 
mission (WCPFC, data available at website) and the 
Inter-American Tropical Tuna Commission (IATTC, 
data available at website) to place Hawaii-based effort 
in an international context. These data are available 
at a 5°x5° horizontal and a monthly temporal resolu¬ 
tion through 2014. The WCPFC provides data for areas 
west of 150°W, and the IATTC provides data for areas 
east of 150°W. This 150°W boundary divides the 2 fish¬ 
ing convention areas of the Hawaii-based fishery. 
Global Ocean Data Assimilation System (GODAS) 
reanalysis data (Saha et al., 2006) provided mod¬ 
eled monthly temperature at depth across the fishing 
grounds for the entire period studied. The GODAS 
data used in this study were provided by the Physical 
Sciences Division of the NOAA Earth System Research 
Laboratory in Boulder, Colorado, and were downloaded 
from the Asia Pacific Data Research Center’s OPeNDAP 
server (website). World Ocean Atlas 2013 (WOA13) data 
(Garcia et al., 2013) provided a 3-dimensional climato¬ 
logical reference of oxygen concentration. The WOA13 
oxygen data were downloaded from the National Cen¬ 
ters for Environmental Information’s OPeNDAP server 
(website). Both the GODAS and WOA13 data sets are 
based on in situ observations such as those from Argo 
floats (Saha et al., 2006) and discrete water samples 
(Garcia et al., 2013). 
Methods 
All data (fishery and environmental), except those 
from the WCPFC and IATTC, were transformed into 
a common l°xl° grid matching that of the WOA13 
data. The GODAS data were changed from their na¬ 
tive 0.33°xl.00° resolution by using nearest coordinate 
regridding. In this study, we examined data at regional 
and quarterly resolutions (e.g., quarter 1 represents 
January, February, and March). The Ferret program 
(NOAA’s Pacific Marine Environmental Laboratory, Se¬ 
attle, WA, website) was used for regridding data. 
We assessed several measures of catch magnitude 
and composition, all in terms of numbers of fish caught 
as opposed to weight of fish. Catch rates were mea¬ 
sured as catch per unit of effort (CPUE), which we de¬ 
fined as the number of fish caught per 1000 hooks set. 
We focused primarily on catch rates of the target spe¬ 
cies, bigeye tuna, and on the primary bycatch species, 
longnose lancetfish. For our assessment of catch com¬ 
position, we used the 21 most commonly caught species 
identified by Polovina and Woodworth-Jefcoats (2013). 
We also followed their method for measuring discard 
rate (measured as the ratio of catch of longnose lancet¬ 
fish, snake mackerel, pelagic stingray ( Pteroplatytrygon 
violacea), and 95% of sharks to total catch). 
We defined preferred thermal habitat for bigeye 
tuna as waters with temperatures of 8-14°C. Tag- 
