Syah et al.: Predicting potential fishing zones for Cololabis saira 
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factors and the migration and distribution of species is 
essential for fisheries management. 
Most studies of Pacific saury have concentrated on 
distribution and migration and have used in situ or 
logbook data (Huang et al., 2007; Tseng et al., 2013), 
and models have been developed to investigate growth 
and abundance (Tian et al., 2004; Ito et al, 2004, 2007; 
Mukaietal., 2007). In contrast, Watanabe et al. (2006) 
proposed a spatial and temporal migration model for 
stock size that was dependent on SST. However, inte- 
grated high-resolution nighttime satellite images, such 
as those available in the time-series data from the Op- 
erational Linescan System (OLS) of the Defense Me- 
teorological Satellite Program, U.S. Department of De- 
fense, together with habitat and environmental model- 
ing, have not been used to predict the potential fishing 
zones for Pacific saury. 
In Japan, fishing vessels operate at night and use 
stick-held dip nets, locally known as bouke ami, which 
are equipped with lights to attract fishes (Fukushima, 
1979). These fishing vessels, equipped with lights, as 
are vessels that fish for Pacific saury, can be identified 
by the OLS sensor, which also enables the detection 
of moonlight-illuminated clouds and lights from cit- 
ies, towns, industrial sites, gas flares, and ephemeral 
events, such as fires and lightning-illuminated clouds 
(Elvidge et al., 1997). In addition, OLS nighttime im- 
ages can be used to estimate fishing vessel numbers 
and fishing areas for squid (Kiyofuji and Saitoh, 2004; 
Kiyofuji et al., 2004). The relationship between the 
number of lit pixels in OLS nighttime images and the 
number of fishing vessels also has been analyzed for 
the fishery of Illex argentinus (Waluda et al., 2002). 
The brightly lit areas seen in nighttime images of the 
western North Pacific are the result of vessels fishing 
for Pacific saury or squid (Semedi et al., 2002; Saitoh 
et al., 2010; Mugo et al., 2014). 
Predictive habitat modeling has become an increas- 
ingly useful tool for marine ecologists and conservation 
scientists in order to estimate the patterns of species 
distribution and to subsequently develop conservation 
strategies (Johnson and Gillingham, 2005; Tsoar et 
al., 2007; Ready et al., 2010). The maximum entropy 
method (Phillips et al., 2006) involves one of the most 
widely used machine-learning algorithms for inferring 
species distributions. In recent studies, the method of 
maximum entropy has been applied to both terrestrial 
(Peterson et al., 2007) and marine ecosystems (Ready 
et ah, 2010; Edren et ah, 2010; Alabia et ah, 2015). In 
this study, we used a maximum entropy approach with 
multi sensor satellite datasets and OLS-derived spe- 
cies occurrences to create an accurate prediction model 
and investigate the potential fishing zones for Pacific 
saury in the western North Pacific. The objectives of 
this study were to evaluate the effects of oceanographic 
factors on the formation of potential fishing zones for 
Pacific saury and to examine the variability in spatial 
patterns of potential fishing zones in relation to the 
prevailing oceanographic conditions in the western 
North Pacific. 
Materials and methods 
Study area 
This study was conducted in the western North Pacific, 
extending from 140° to 155°E and from 34° to 46°N 
(Fig. 1). In this study area, located between the sub- 
arctic and subtropical domains of the North Pacific, 
the confluence of the warm Kuroshio Current and the 
cold Oyashio Current forms the Kuroshio-Oyashio 
transition zone (Roden, 1991), also called the subarc- 
tic-subtropical transition zone. The Kuroshio Current 
is characterized by warm, low-density, nutrient-poor, 
and high-salinity surface waters (Yatsu et al., 2013), 
whereas the Oyashio Current is characterized by low- 
salinity, low-temperature, and nutrient-rich waters 
(Sakurai, 2007). The Kuroshio-Oyashio transition 
zone is characterized by the mixing of various water 
masses and complex physical oceanographic structures 
(Roden, 1991). Moreover, 3 major oceanic fronts exist 
in this region: the Polar Front, Subarctic Front, and 
Kuroshio Extension Front (Science Council of Japan^). 
The characteristic patterns of these oceanic fronts also 
have been well documented in earlier studies (Kitano, 
1972; Roden et al., 1982; Belkin and Mikhailichenko, 
1986; Miyake, 1989; Belkin et ah, 1992, 2002; Yoshida, 
1993; Onishi, 2001; Murase et ah, 2014; Shotwell et 
al., 2014). 
Satellite nighttime images 
Daily cloud-free OLS nighttime images were download- 
ed from the Satellite Image Database System of the 
Agriculture, Forestry and Fisheries Research Informa- 
tion Center of the Japan Ministry of Agriculture, For- 
estry and Fisheries [the system is no longer operating]. 
The images were then used to determine the location 
of the vessels that fish for Pacific saury in the western 
North Pacific. A TeraScan^ system, vers. 4.0 (Seaspace 
Corp., Poway, CA) was used to analyze the images and 
to process the nighttime lights into digital numbers 
(DNs), in a range of 0-63, that represent the visible 
pixels in relative values. We selected 1264 single pass 
images collected from August through December dur- 
ing 2005-2013 (9 years) by 6 Defense Meteorological 
Satellite Program satellites (F13, F14, F15, F16, F17, 
and F18) (Table 1). The period from August through 
December was chosen for analysis because it corre- 
sponds with the fishing season of Pacific saury. To con- 
struct the habitat suitability model, the daily images 
were reprocessed with a 1-km resolution and then com- 
piled in a monthly database. The location of the vessels 
was assumed to represent the location of Pacific saury. 
1 Science Council of Japan. 1960. The results of the Japa- 
nese oceanographic project for the International Geophysical 
Year 1957/8, 145 p. National Committee for the Interna- 
tional Geophysical Year, Science Council of Japan, Tokyo. 
^ Mention of trades names or commercial companies is for 
identification purposes only and does not imply endorsement 
by the National Marine Fisheries Service, NOAA. 
