Jurnal OSeanografi Fisika

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Habitat characteristics of skipjack tuna (Katsuwonus pelamis)in the western North Pacific: a remote sensing perspective

ROBINSON MUGO,1,2,* SEI-ICHI SAITOH,1

AKIRA NIHIRA3 AND TADAAKIKUROYAMA3

1Laboratory of Marine Environment and Resource Sensing,

Graduate School of Fisheries Sciences, Hokkaido University,

3-1-1 Minato-cho, Hakodate, 041-8611, Hokkaido, Japan2Kenya Marine and Fisheries Research Institute,P.O. Box 81651, Mombasa, Kenya3Ibaraki Prefecture Fisheries Research Station, Hitachinaka,

Ibaraki, Japan

ABSTRACT

Skipjack tuna habitat in the western North Pacific was

studied from satellite remotely sensed environment

and catch data, using generalized additive models and

geographic information systems. Weekly resolved re-

motely sensed sea surface temperature, surface chlo-

rophyll, sea surface height anomalies and eddy kinetic

energy data were used for the year 2004. Fifteen gen-

eralized additive models were constructed with skip-

jack catch per unit effort as a response variable, and

sea surface temperature, sea surface height anomaliesand eddy kinetic energy as model covariates to assess

the effect of environment on catch per unit effort

(skipjack tuna abundance). Model selection was based

on significance of model terms, reduction in Akaikes

Information Criterion, and increase in cumulative

deviance explained. The model selected was used to

predict skipjack tuna catch per unit effort using

monthly resolved environmental data for assessing

model performance and to visualize the basin scale

distribution of skipjack tuna habitat. Predicted values

were validated using a linear model. Based on the four-

parameter model, skipjack tuna habitat selection wassignificantly (P < 0.01) influenced by sea surfacetemperatures ranging from 20.5 to 26C, relatively

oligotrophic waters (surface chlorophyll 0.080.18,

0.220.27 and 0.30.37 mg m)3), zero to positive

anomalies (surface height anomalies 050 cm), and

low to moderate eddy kinetic energy (0200 and 700

2500 cm2 s2). Predicted catch per unit effort showed

a trend consistent with the northsouth migration of

skipjack tuna. Validation of predicted catch per unit

effort with that observed, pooled monthly, was sig-

nificant (P < 0.01,r2 = 0.64). Sea surface temperatureexplained the highest deviance in generalized additive

models and was therefore considered the best habitat

predictor.

Key words: generalized additive models, geographicinformation systems, habitat characterization, remote

sensing, skipjack tuna, western North Pacific

INTRODUCTION

Skipjack tuna (Katsuwonus pelamis) is a highly migra-tory pelagic species inhabiting all tropical and sub-

tropical waters of the worlds oceans (Matsumoto,

1975; Arai et al., 2005). The species is commerciallyimportant, ranked among the first 10 species that have

contributed highly to global catches in previous years(FAO, 2009). A significant portion of these catches

are from the Pacific Ocean, which has one of the most

productive fisheries in the world, particularly the

western North Pacific. Skipjack tuna catches from

the Pacific Ocean have increased consistently since

the 1980s (Miyake et al., 2004). Despite the highcatches and exploitation rates, the western Pacific

stock is said to be capable of sustaining even larger

catches (Lehodey et al., 1998). Catches are highestfrom May to August off Japan (Wild and Hampton,

1993). Katsuwonus pelamis are caught almost entirely

by surface gears such as pole and line (Langley et al.,2005) and purse seines, although other miscellaneousgears are also used (Miyake et al., 2004).

Distribution in sub-tropical waters is confined to

the 15C surface temperature isotherm (Wild and

Hampton, 1993). In the western Pacific, skipjack tuna

have been captured as far as 44N off Japan (Wild and

Hampton, 1993; Langley et al., 2005). Migration pat-terns in the western North Pacific follow a north

south seasonal cycle where the poleward movement

occurs in the fallsummer season (Kawai and Sasaki,

*Correspondence. e-mail: [email protected].

[email protected]

Received 16 May 2009

Revised version accepted 18 May 2010

FISHERIES OCEANOGRAPHY Fish. Oceanogr. 19:5, 382396, 2010

382 doi:10.1111/j.1365-2419.2010.00552.x 2010 Blackwell Publishing Ltd.


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1962; Matsumoto, 1975; Watanabe et al., 1995;Ogura, 2003). This migration is also influenced by

ocean currents and the fish move along prevailing

currents, utilizing them as foraging habitats (Uda and

Ishino, 1958; Uda, 1973). The westernmost groups

comprise one originating from the Philippine islands

and a second group from the MariannaMarshall is-lands. These groups migrate northwards along the

Japanese coastal waters (Fig. 1). The third group

originates east of the Marshall Islands and moves

northwest into Japanese offshore waters (Matsumoto,

1975). Part of this group could move farther down-

stream of the Kuroshio to the east of Midway Island. In

late summer and early autumn, the fish begin their

southward migration. Skipjack tuna are known to

associate with fronts, warm-water streamers and eddies

during their northward migration from sub-tropical to

temperate waters (Tameishi and Shinomiya, 1989;

Sugimoto and Tameishi, 1992). We hypothesized thatskipjack tuna were utilizing oceanographic features

recognizable from satellite remotely sensed data, and

that such information could be used in a multivariate

model to derive habitat signatures for the species in

the western North Pacific.

Skipjack tuna physiology and morphology play a

major role in determination of habitat and, by

extension, distribution (Wild and Hampton, 1993).

They lack a swim bladder, which allows for rapid

vertical movements within the near surface habitat.

They also have high oxygen demands (33.5 mL L1)

(Barkley et al., 1978) due to high metabolic rates.Oxygen concentration usually approaches saturation

(4.5 mL L1) in surface waters, but is often less than

the minimum requirement for skipjack (2.45 mL L)1)

in waters below the thermocline (Wild and Hampton,

1993), restricting skipjack tuna mainly to the mixedlayer above the thermocline. This makes oceano-

graphic satellite observations ideal for habitat studies

for such a species. Nihira (1996) explained a size

screening mechanism for migration of skipjack tunas

across the Kuroshio Front, a phenomenon where only

fish above 45 cm in fork length are able to move from

the Transitional Zone, across the front, and into the

southern area of the Sub-tropical Counter Current

during the southward migration. This was due to their

ability to raise their body temperature while in the

transitional area. Those smaller than 45 cm were

incapable of raising their body temperature and thusremained in the Transitional Zone.

Global demand for fish is exerting more pressure on

fish stocks, in addition to climate change-induced

impacts (changes in species distributions and disrup-

tion of marine ecosystems) (Cheung et al., 2009).Developing robust tools for near-real time habitat

assessments will facilitate prediction of stocks re-

sponses to externalities such as climate change and

fishing pressure. In many studies (Saitoh et al., 1986;Sugimoto and Tameishi, 1992; Nihira, 1996; Andrade

Figure 1. Schematic illustration of the northern migration of skipjack tuna in the western North Pacific, off the southeast and

east coasts of Japan. The northern migration routes of different groups of skipjack tuna are shown with green lines (14). The

typical path of the Kuroshio Current is indicated by the continuous red line; the Oyashio Current is shown in blue. Figure

modified from Nihira (1996).

Skipjack tuna habitat from RS & GIS in western NP 383

2010 Blackwell Publishing Ltd, Fish. Oceanogr., 19:5, 382396.


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and Garcia, 1999; Andrade, 2003) temperature has

been the main environmental variable used to explain

skipjack tuna occurrence and abundance. Although

temperature is important, other factors such as chlo-

rophyll concentration and ocean mesoscale variability

could have direct or indirect effects on forage distri-bution and hence on the distribution of apex preda-

tors. Chlorophyll concentration is good indicator of

albacore tuna habitats (Laurs et al., 1984; Zainuddinet al., 2008), and mesoscale variability is known toinfluence catch per unit efforts (CPUEs) of albacore

tuna (Domokos et al., 2007) and foraging habitat forseabirds (Nel et al., 2001). Some of these variablesmay synergistically form suitable habitats for pelagic

species.

Generalized additive models (GAMs) were used to

model skipjack tuna habitats from catch data and

satellite remotely sensed oceanographic data. A GAM(Hastie and Tibshirani, 1990) is a semi-parametric

extension of a generalized linear model, with an

assumption that the functions are additive and that

the components are smooth (Guisan et al., 2002). Ituses a link function to establish a relationship between

the mean of the response variable and a smoothed

function of the explanatory variable(s). The strength

of GAMs lies in their ability to deal with highly non-

linear and non-monotonic relationships between the

response and the set of explanatory variables. This

makes them ideal for expressing underlying relation-

ships in ecological systems. Statistical models and GIS

(Valavaniset al., 2008) are tools with the potential toenhance species habitat research. The objective of this

work was to study skipjack tuna habitat from multi-

sensor satellite remotely sensed environment and

fishery data, using GAMs and GIS.

MATERIALS AND METHODS

Study area

This study was conducted in the western North Pacific

(1850N and 125180E) (Fig. 1), an area where

Japanese skipjack tuna fishing vessels operate off the

east coast of Japan. It is a productive ecosysteminfluenced mainly by the Tsugaru Warm Current, the

Oyashio Current and the Kuroshio Current (Talley

et al., 1995). The Tsugaru Warm Current originatesfrom the Tsushima Current and flows with warm and

saline water from the Sea of Japan (Talley et al.,1995). The Oyashio waters, formed from the Okhotsk

Sea and the Western Sub-arctic Gyre (Yasuda, 2003),

flow southward, transporting low temperature, low

salinity and nutrient-rich waters to the sub-tropical

gyre (Sakurai, 2007). The Oyashio commonly mean-

ders twice after leaving the coast of Hokkaido, gen-

erating the first and second intrusions (Kawai, 1972).

The meanders are separated by a warm core ring

(WCR) originating from the northward movement of

the ring produced by the Kuroshio (Yasuda et al.,

1992). The southern limit of sub-polar waters is oftenreferred to as the Oyashio Front (Talley et al., 1995).The Oyashio ecosystem is an important fishing ground

for several sub-arctic species and sub-tropical migrants

(Saitohet al., 1986). The Kuroshio originates from thesub-tropical gyre and is distinguished by low density,

nutrient-poor, warm and high salinity surface waters

(Kawai, 1972; Talley et al., 1995). The KuroshioExtension is an eastward-flowing inertial jet charac-

terized by large-amplitude meanders and energetic

pinched-off eddies, with high eddy kinetic energies

(Qiu, 2002). Confluence of the two currents results in

a mixed region, the KuroshioOyashio TransitionZone (Yasuda, 2003). The behavior of the Kuroshio

Extension, warm streamers and WCRs in the Transi-

tion Zone is important to the fishing industry (e.g.,

Saitoh et al., 1986; Sugimoto and Tameishi, 1992).

Fishery data

Skipjack tuna daily catch data obtained from the

Ibaraki Prefecture Fisheries Research Station, for the

period March to November (2004) were digitized from

fishing logs of a pole and line fishery (173 vessels) and

compiled into a database. These data comprised daily

geo-referenced fishing positions (latitude and longi-

tude), catch in tonnes and effort, from which catch perunit effort (CPUE) was determined in tonnes per boat

day. The data were mapped using ARCGIS 9.2 ( ESRI,

Redlands, CA, USA) and further compiled into

weekly and monthly resolved datasets.

Remotely sensed environmental data

Weekly and monthly environment databases were

compiled for sea surface temperature (SST), sea sur-

face chlorophyll (SSC), sea surface height anomaly

(SSHA) and eddy kinetic energy (EKE). Daily SST

and SSC, Moderate Resolution Imaging Spectro-

radiometer (MODIS) Aqua standard mapped images(SMI) for the year 2004, with a spatial resolution

of approximately 4.63 km were downloaded from

the PO.DAAC (http://podaac.jpl.nasa.gov) and the

Ocean Color (http://oceancolor.gsfc.nasa.gov) sites

respectively, and composited into 7-day images using

SEADAS version 5.3 (NASA, Greenbelt, MD, USA).

Given that it is extremely challenging to match daily

fishery data to daily chlorophyll images (which usually

have very sparse data due to clouds), we used 7-day

composite images. The 7-day MODIS SST and SSC

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images also matched the temporal scale for SSHA and

geostrophic velocities from AVISO (Archiving, Vali-

dation and Interpretation of Satellite Oceanographic

data) which have a weekly temporal resolution. Nine-

monthly (MarchNovember) SST and SSC SMI were

also downloaded from the PO.DAAC and OceanColor sites, respectively. Weekly mean sea level

anomaly data were downloaded from the AVISO

(http://www.aviso.oceanobs.com), into ARCGIS 9.2

using the Marine Geo-spatial Ecology Tool (MGET)

(Roberts et al., in press), a custom made add-in pro-gram for marine-GIS applications. We downloaded

the delayed time, updated and merged product of

mean sea level anomaly. The data are global images

with a 13 resolution. They were re-sampled to the

SST and SSC resolution and subset to the study area,

using ARCGIS 9.2. The weekly SSHA images were

averaged to monthly images in SEADAS 5.3. Weeklyglobal geostrophic current velocity images, 13

(u and v components) were downloaded as ARCGISrasters from AVISO using the MGET. The u and vweekly rasters were used to calculate EKE with the

Raster Calculator function in Spatial Analyst exten-

sion (ARCGIS 9.2) using Eqn 1 (Robinson, 2004). The

calculated EKE rasters were subset to the study area.

The weekly images were averaged to monthly EKE in

SEADAS 5.3.

EKE 1=2u2 v2 1

Matching fishery data to remotely sensed environment dataWeekly resolved skipjack tuna data were matched to

corresponding images for SST, SSC, SSHA and EKE

using a C-shell script. The ship-track function in

SEADAS 5.3 was used to extract values corresponding

to latitude and longitude positions from the fishery

dataset. The result was a full matrix of CPUEs and the

respective environmental variables (Valavanis et al.,2008). This matrix was used to fit GAMs.

Generalized additive models

GAMs were constructed in R (version 2.7.2) software,

using the gam function of the mgcv package (Wood,2006), with CPUE as the response variable and SST,

SSC, SSHA and EKE as predictor variables. A model

of the form shown in Eqn 2 was applied

gui a0 s1x1is2x2is3x3i :::snxni; 2

where g is the link function,uiis the expected value ofthe dependent variable (CPUE), a0 is the model con-

stant, and sn is a smoothing function for each of themodel covariatesxn(Wood, 2006). The CPUE followsa continuous distribution; therefore, we chose the

Gaussian family which is associated with the identity

link function. We used a logarithmic transformation on

CPUEs to normalize the asymmetrical distribution

(Zainuddin et al., 2008). A factor of 0.1 was addedbefore log-transformation to account for zero CPUEs

(Howell and Kobayashi, 2006). Models were con-structed from the simplest form using one independent

variable, e.g., SST only, with subsequent addition of

predictor variables. Model selection was based on sig-

nificance of predictor terms, deviance explained, and

reduction in Akaike Information Criterion (AIC)

value (Johnson and Omland, 2004). Constructed

GAMs can be used to predict skipjack tuna CPUEs

using thepredict.gamfunction inmgcvpackage, given aset of covariates similar to those used to build the

model. Such an approach was employed by Howell and

Kobayashi (2006) and Zagagliaet al.(2004). We made

predictions from the best model selected from a set of15 models. Due to sparseness of data on SST and SSC

weekly images, we made predictions from monthly

composites of the four environmental predictors.

Spatial mapping and validation of predicted CPUEs

Predicted CPUEs were mapped using GENERICMAPPING

TOOLS (Wessel and Smith, 1998) (GMT 4.4.0) and

subsequently overlain with observed CPUEs. The

mapped grids were sampled using the latitude and

longitude positions of the observed CPUEs, thus cre-

ating a matrix of observed versus predicted CPUEs.

We further compared observed and predicted CPUEs,

pooled monthly, using a linear model.

RESULTS

Temporal variability of CPUE and environmentalvariables

The monthly spatial distribution of fishing sets from

March to November 2004 relative to oceanographic

variables is shown in Fig. 2ad. Figure 3 exemplifies

the weekly fishing ground-oceanographic environment

relationship in September (week 38) where the asso-

ciation with SST and SSC gradients and eddies is

more apparent. The fishing fleet moved north fromMarch to August, and south from September to

November. The latitudinal displacement is shown in

Fig. 4a. Figure 4bf presents weekly time series plot of

mean CPUE, SST, SSC, SSHA and EKE. The mean

CPUE (Fig. 4b) increased gradually until week 27,

after which there was a decline. The lowest mean

CPUE values were recorded in the last weeks of the

fishing season (OctoberNovember).

Mean SST rose to 27.8C (week 29) (Fig. 4c) and

thereafter declined to 17.5C in the last week. The




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mean SSC concentration was lowest in the 12th week

(0.09), after which it increased to about 0.4 mg m3

(week 17), with sharp rises in November (Fig. 4d).

Mean SSHA (Fig. 4e) shows a pattern where anoma-

lies were positive (week 1133). During this period

(MarchJune), the fishing fleet was at or south of the

Kuroshio Front. Anomalies from week 34 to 44 were

all negative, a period when the fleet had sailed beyond

(a) (b) (c) (d)

Figure 2. Spatial distribution of skipjack tuna fishing locations overlaid on 9-monthly images for each of the four environmental

variables (a) SST, (b) SSC, (c) SSHA and (d) EKE). Fishing locations are shown as red dots.

386 R. Mugoet al.



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the Kuroshio area. The mean EKE (Fig. 4f) ranged

from 110 to 2315.7 cm2 s2. All mean EKE values

were below 1000 cm2 s2 except for weeks 13, 14, 17,

26, 30, 31 and 41.

Distribution of CPUE and habitat variables from skipjacktuna fishing sets

Distributions of CPUE and the four environmental

variables utilized by skipjack tuna in the western

North Pacific in 2004 are shown in Fig. 5. The

distribution of CPUE was asymmetrical (Fig. 5a). A

log-transformation of CPUEs indicated that our

assumption to transform the data was appropriate

(Fig 5b). Skipjack tuna were caught between 16 and

30C SST, with the highest frequency of fishing sets

occurring at 20 and 25C (Fig. 5c). Chlorophyll-a

concentration range for fishing sets was 02 mg m3,

with 0.10.4 mg m3 being the preferred concentra-

tion (Fig. 5d). The range for SSHA was )30 to 80 cm,

with )20 to 30 cm showing the highest frequency of

fishing sets, with a peak at zero (Fig. 5e). Distribution

of EKE was asymmetrical, with the highest frequencyof fishing sets between zero and 200 cm2 s2 (Fig. 5f).

GAM-derived habitat characteristics for skipjack tuna

Results for each of the 15 models (model, predictor

variables used to construct it, the respective degrees of

freedom, AIC, P-value and deviance explained) areshown in Table 1. The table presents single parameter

models constructed from one predictor variable, two-

parameter models, made from a combination of any

two predictor variables, three-parameter models from

Figure 3. Fishing positions (gray dots)

in one of the weeks in September 2004

(week 38) overlaid on weekly averaged

SST, SSC, SSHA and EKE. The 20CSST and a 0.3 mg m3 SSC contour (red

line) are plotted on the respective ima-

ges to emphasize SST and SSC gradients.

Eddies are observable on the SSHA and

EKE images.




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three predictor variables and four-parameter models

from all variables. In all the models, the predictor

variables were highly significant (P < 0.01). Singleparameter models had the lowest deviance explained,

especially for SSHA and EKE. SST showed the highest

deviance explained among the single parameter

models, then chlorophyll-a. Addition of predictorvariables at different levels resulted in slight increase

or decrease in deviance explained, while the AIC

value varied. For instance a model constructed from

SSHA and EKE shows the two may not account for

much variability in skipjack tuna CPUE. SST and

EKE, or SST and SSC account for relatively higher

variability in CPUE, according to AIC and deviance

explained. Among the three-parameter models, com-

bination of SST, SSC and EKE showed that all pre-

dictor variables were highly significant, had the lowest

AIC value and the highest deviance explained

(12.8%). Overall, the four-parameter model had the

lowest AIC value and the highest deviance explained.

(a) (b)

(d)

(f)(e)

(c)

Figure 4. Time-series plots of weekly averaged (a) latitudinal displacement of fishing positions, (b) CPUE, (c) SST, (d) SSC,

(e) SSHA and (f) EKE.

388 R. Mugoet al.



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(a) (b)

(d)

(f)(e)

(c)

Figure 5. Histograms of CPUE and environmental variables showing (a) distribution of CPUEs; (b) distribution of log-trans-

formed CPUE, which helped to normalize the asymmetrical distribution observed in (a); and (c) utilization of SST, (d) SSC, (e)

SSHA and (f) EKE by skipjack tuna in the western North Pacific in 2004.




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A normal quantilequantile (QQ) plot fitted to

examine sample versus theoretical quantiles shows a

nearly straight 1 : 1 line (Fig. 6), implying that the

application of a Gaussian distribution was ideal.

A QQ plot is useful for assessing the relationship of

sample to theoretical quantiles (Wood, 2006).

GAM plots (Fig. 7) can be interpreted as the

individual effect of each predictor variable on CPUE.

Rug plots on the horizontal axis represent observeddata points and the fitted function is shown by the

thick line. The gray shade shows the 95% confidence

interval. A negative effect on CPUE was observed

from SST values of 1620.5C. From 20.5 to 21C

there was a sharp positive effect on CPUE. Beyond

26C, the plot shows a decline, because the number of

sets done within 2630C is low. Consequently the

confidence intervals are wider. For SSC, a positive

effect on CPUE is evident from 0.080.18 mg m3,

0.220.27, and 0.30.37 mg m3 (Fig. 7b). A decline

occurs towards higher SSC values over 0.4 mg m3.

The plot on SSHA (Fig. 7c) shows a range from )40

to 80 cm, with fewer data points on the extremes.

A positive effect on CPUE was noted from 0 to 50 cm.

The plot on EKE shows a high density of data points

between 0 and 1200 cm2 s2 (Fig. 7d), with a positive

effect on CPUE on values below 200 cm2 s2, also

with the highest number of fishing sets. In addition,

7002500 cm2

s2

shows a positive effect over themean, but these values (especially after 1000 cm2 s2)

represent fewer data.

Prediction and validation of CPUEs

Predicted and mapped CPUEs are shown in Fig. 8.

The predicted CPUE for skipjack tuna show a north-

ward displacement from March (around 25N),

extending to about 41N in September, after which

there is a southward displacement. In October and

November, the areas with relatively high CPUEs are

Table 1. GAMs fitted in the model

selection process (N = 6747). The

model, predictor terms used, the esti-

mated degrees of freedom, Akaike

information criterion (AIC) value and

percent cumulative deviance explained.The best model was selected based on

significance of predictor terms, reduction

of AIC and increase in cumulative

deviance explained (CDE).

Model Variable EDF AIC P-value CDE%

SST SST 8.3 4593.50 < 2.00 1016 9.9

SSC SSC 8.7 4736.30 < 2.00 1016 8

SSHA SSHA 8.2 5052.40 < 2.00 1016 3.6

EKE EKE 8.1 5003.40 < 2.00 1016 4.3SST+SSC SST 7.8 4527.90 < 2.00 1016 11

SSC 8.6 8.09 1014

SST+SSHA SST 8.2 4548.80 < 2.00 1016 10.7

SSHA 7.7 1.84 1009

SST+EKE SST 8.2 4479.30 < 2.00 1016 11.6

EKE 8.2 < 2.00 1016

SSC+SSHA SSC 8.7 4659.00 < 2.00 1016 9.26

SSHA 8.2 1.33 1015

SSC+EKE SSC 8.7 4544.40 < 2.00 1016 10.8

EKE 8.2 < 2.00 1016

SSHA+EKE SSHA 6.4 4829.30 < 2.00 1016 6.9

EKE 8.2 < 2.00 1016

SST+SSC+SSHA SST 7.8 4488.10 < 2.00 1016 11.7

SSC 8.7 1.19 1012

SSHA 8 2.24 1008

SST+SSC+EKE SST 7.7 4402.40 < 2.00 1016 12.8

SSC 8.6 < 6.27 1016

EKE 8.2 < 2.00 1016

SST+SSHA+EKE SST 8.1 4431.10 < 2.00 1016 12.3

SSHA 2.8 8.68 1011

EKE 8.4 < 2.00 1016

SSC+SSHA+EKE SSC 8.7 4502.90 < 2.00 1016 11.5

SSHA 5 1.09 1008

EKE 8.3 < 2.00 1016

SST+SSC+SSHA+EKE SST 7.6 4371.10 < 2 .00 1016 13.3

SSC 8.7 9.93 1013

SSHA 2.5 2.59 1007

EKE 8.3 < 2.00 1016

390 R. Mugoet al.



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extending southwards. The predicted values lie be-

tween zero and 2 tonnes per boat day. Correlation of

observed and predicted CPUEs pooled monthly

showed a significant relationship (P < 0.01;r2 = 0.64)(Fig. 9).

DISCUSSION

We combined GAMs and GIS to study skipjack tuna

habitat from fishery data and satellite remotely sensed

datasets in the western North Pacific. Fishing data,

though not free from sampling bias based on the

fishermens choice of fishing locations, are low-cost

species distribution data sets available to fishery sci-

entists. Fishing data CPUE are often used as an index

of fish occurrence and abundance (Lehodey et al.,1998) and therefore high CPUEs can be said to indi-

cate preferred oceanographic conditions for a species.

Here we further examine our results and their inherentrelevance as environmental indicators of skipjack tuna

habitat.

During weeks 1729, the fishery operated between

30 and 35N (Fig. 4a), a period when the mean weekly

SSTs (Fig. 4c) are increasing as a result of rising sur-

face water temperatures due to flow of warm waters

transported by the Kuroshio Current. This is also re-

flected by the mean SSC, which shows that the waters

were correspondingly oligotrophic (Fig. 4d). However,

after July the fishery advanced further north, where the

waters get progressively cooler and eutrophic owing to

the intrusion of Oyashio waters. This may partly ex-

plain the decline in mean SSTs towards November.

The northeastward migration of skipjack tuna is

thought to be strongly influenced by temperature

(Uda, 1973; Sugimoto and Tameishi, 1992; Nihira,1996), which can be observed through rising SSTs in

the north, and the gradual extension of the Kuroshio

Current. According to Uda (1973), when the warm

Kuroshio water spreads over a broader northern area,

more skipjack become available to the Japanese fish-

ery, but a strong Oyashio Current hinders skipjack

tuna migration and leads to a lower catch. Ito et al.(1998) also showed that temperature affected the

migration pattern of skipjack tuna. Temperature as a

habitat signature may explain part of the observed

spatio-temporal variability in skipjack tuna fishing set

distribution and CPUE; indeed, SST is known forinfluencing tuna migration (Sund et al., 1981). Ourwork shows that SST significantly influenced skipjack

CPUE (abundance). The results are consistent with

previous findings in the western North Pacific, where

optimum SST range was 20.523C in the Kureshio

region (Mishra et al., 2001) and 2226.5C in thesouthwest Atlantic (Andrade and Garcia, 1999).

However, our results also show a broader range of SST,

from 17 to 26.5C. A possible explanation for this

discrepancy could be the northward migration of

skipjack tuna in distinct groups (Nihira, 1996) that

occupy varying SST regimes (Fig. 2a).

Temperature limits horizontal and vertical distri-bution of skipjack tuna, and this varies by region and

size (Sundet al., 1981). Loukoset al.(2003) suggestedsignificant large-scale changes of skipjack habitat in

the equatorial Pacific due to increased ocean temper-

atures caused by global warming. Such a scenario

could expand available habitat for warm water pelagics

to higher latitudes (Cheung et al., 2009). Ogura(2003) reported that tagged skipjack tuna in the

western North Pacific showed daily vertical move-

ments, where they spent much of the time near the

surface during night time, swimming in deeper waters

with occasional dives during the day. Night timedepths ranged from surface to 30 m, whereas day time

dives often were beyond 100 m. The fish appeared to

avoid waters below 17C. In the equatorial Pacific,

skipjack tuna spent 98.6% of their time above the

thermocline (depth = 44 m) during the night but less

time (37.7%) below the thermocline during the day

(Schaefer and Fuller, 2007). According to the study,

one fish swam in waters where the ambient tempera-

ture reached 10.5C, while the peritoneal cavity

temperature reached 15.9C. Such tagging experi-

Figure 6. A normal QQ plot for the four-parameter model

showing a plot fitted to examine sample versus theoretical

quantiles. The nearly straight 1 : 1 line of the plotted points

shows that the sample distribution is very similar to a stan-

dard normal distribution. The model was constructed using a

Gaussian distribution.




11/15

ments are important for understanding skipjack tuna

vertical habitat utilization. Our results (based on

SSTs) indicate that few fishing sets (< 5%) occurred at

temperatures between 16 and 17C (Fig. 5c).

Chlorophyll concentration, the only biological

component available to satellite remote sensing, is anindex of phytoplankton biomass which provides valu-

able information about trophic interactions in marine

ecosystems (Wilsonet al., 2008). Skipjack tuna fishingsets occurred in waters with relatively low SSC

(Fig. 5d). Previous work shows that skipjack tuna in

the western North Pacific were caught within relatively

low chlorophyll waters which corresponded to warm

water SSTs (Wilson et al., 2008). Preference for rela-tively low chlorophyll waters, especially on the frontal

edges of warm oligotrophic waters has both physio-

logical and trophic implications. This enables skipjack

tuna to not only locate and forage on the periphery of

highly productive frontal or upwelling zones, but also

stay within tolerable temperatures (Ramos et al.,1996). Nihira (1996) reported that stomach contents

of skipjack tuna caught near the front of a warmstreamer were two to five times heavier than those

caught at the center of the warm streamer and thus

concluded that the front was the most suitable feeding

place. Fiedler and Bernard (1987) found that skipjack

tuna aggregated on the warm edge of waters near cold

and productive water masses. They concluded that this

enabled skipjack tuna to feed on large numbers of

euphausiids found in upwelled cold waters. A similar

conclusion was reached by Andrade (2003), indicating

that areas with thermal or color gradients provide both

(a) (b)

(c) (d)

Figure 7. GAM-derived effect of the four oceanographic variables on CPUE, from the model constructed with: (a) SST, (b)

SSC, (c) SSHA and (d) EKE. Gray-shaded area indicates the 95% confidence intervals; the solid line shows the fitted GAM

function which describes the effect that a predictor variable has on the response variable (CPUE). The relative density of data

points is shown by the rug plot on the x-axis. Values of a predictor variable showing a positive effect on CPUE were read as all

values for which the fitted GAM function was above the zero axis.

392 R. Mugoet al.



12/15

physiological and foraging advantages. While high

tuna abundances occur close to highly productive

frontal and upwelling zones, primary production per sedoes not aggregate tuna (Lehodeyet al., 1998). It is thedownstream development of secondary production

that provides attractive habitat for skipjack tuna

(Roger, 1994; Lehodeyet al., 1998).Oceanic fronts are broadly understood to mark

the boundary between two different water masses,

manifested as regions of strong horizontal gradients in

temperature, salinity, chlorophyll, and concentration

of zooplankton and micronekton (Olson et al., 1994;Kirby et al., 2000). Our results indicate that the

highest frequencies of skipjack tuna fishing sets werewithin 0.10.3 mg m3, with a peak frequency at

0.2 mg m3 (Fig. 5d). However, later in the year

(AugustNovember), the fishing fleet was more closely

associated with the 0.3 mg m3 SSC isopleth (e.g.,

Fig. 3). The 0.2 mg m3 isopleth in the North Pacific,

a proxy for the transition zone chlorophyll front

(TZCF), has been shown to be a major feature

important for migration and foraging of apex predators

such as tunas (Polovina et al., 2001; Polovina andHowell, 2005). Reasons proposed to account for the

association of tunas with fronts include: (i) confine-

ment to a physiologically optimum temperature range;(ii) use of frontal gradients for thermoregulation; (iii)

limitation of visual hunting efficiency owing to water

clarity; and (iv) the availability of appropriate food

(Kirby et al., 2000). Frontal systems are importantaggregating mechanisms for plankton and micronek-

ton (Laurs et al., 1984; Lehodey et al., 1998). Tunasare predominantly visual predators, feeding opportu-

nistically and unselectively on micronekton (Black-

burn, 1968). Highly turbid waters are therefore

unsuitable for them (Laurs et al., 1984; Ramos et al.,

Figure 8. Predicted skipjack tuna CPUEs overlaid with observed fishing locations from March to November, 2004. Predictions

were done on monthly averaged images due to lack of data in many of the weekly images, especially for SST and SSC.

Figure 9. A scatter plot of pooled monthly observed against

predicted CPUE values (P < 0.01, r2 = 0.64).




13/15

1996; Kirby et al., 2000), and extremely oligotrophicwaters would contain little food (Sund et al., 1981).Prey abundance and water clarity affect the rate of

food encounter (Kirby et al., 2000).We used SSHA data to address the relationship

between skipjack tuna habitat and mesoscale vari-ability. Among the four variables used to construct

GAMs, SSHA was the least significant (Table 1).

Predominantly positive SSHAs had a greater effect on

CPUE (Fig. 7c), implying preference for areas either

with zero anomalies or closely associated with warm

eddies. Zagagliaet al.(2004) also found SSHA to havehad the least influence on yellofin tuna CPUE, among

two other environmental variables, SST and surface

chlorophyll-a. They attributed this finding to thepossibility that SSHA could have varying effects on

aggregation of fishery resources, owing to its complex

combination of dynamical and thermo-dynamicalfactors, thus its influence on distribution and abun-

dance of pelagic resources may be less apparent.

Eddy kinetic energy results suggest that skipjack

tuna fishing sets were made in areas with low to

moderate EKE, indicating that there were instances

when catches were made in areas associated with ed-

dies (e.g., Fig. 3). In the western North Pacific, eddies

have been shown to influence formation of skipjack

tuna fishing grounds (Sugimoto and Tameishi, 1992).

In the Gulf of Mexico, Bluefin tuna significantly pre-

ferred areas with moderate EKEs (251355 cm2 s2)

(Teo et al., 2007), while in the American Samoa

albacore, tuna catches were higher at eddy edges(Domokos et al., 2007). Mechanisms leading toaggregation of tuna along eddy edges include nutrient

injection or entrainment to the euphotic zone (Olson,

1991) and development of phytoplankton blooms

which trigger secondary production (Bakun, 2006).

This attracts nekton, with a net effect of aggregation

of apex predators to forage on the lower trophic level

organisms around the eddy edge (Ramos et al., 1996;Fonteneauet al., 2009).

Predicted CPUEs show an appreciable concurrence

with actual fishing locations for the months May to

August (Fig. 8). This is also the period that showedconsistent increases in mean CPUE over the study

period, suggesting that the model performed fairly well

in predicting areas that showed increasing abundances.

However, given that the range of observed CPUEs was

>2 tonnes per boat day (Fig. 5a), the model appears to

have difficulties in predicting higher catch rates. The

relationship between observed and predicted CPUEs

was significant. Zagaglia et al. (2004) also reported asignificant relationship between observed CPUEs and

those predicted from a GAM (r = 0.5073) for yel-

lowfin tuna in the equatorial Atlantic Ocean. Howell

and Kobayashi (2006), working on GAMs for longline

big eye tuna fishery in the Palmyra Atoll, found a

significant relationship between observed and pre-

dicted catch rates (r = 0.35) on a set by set basis, and

on monthly averaged catch rates (r = 0.78), but re-ported difficulties predicting low and high catches in

their model. They suggested that additional data with

enhanced biological characteristics were likely to im-

prove such models. Our model explained 13.3% of

variability in skipjack tuna abundance and was based

on environmental variables only, which are important

habitat predictors but probably not the only factors

influencing fishing locations for skipjack tuna. In

addition, data that include more years are likely to

generate a more robust model with greater predictive

power. The models were constructed from weekly re-

solved data sets due to absence of valid daily SST andSSC data. We suggest that better habitat signatures

could be derived from satellite data with improved

temporal resolution and coverage.

CONCLUSIONS

We conclude that:

SST was the most important habitat predictor for

skipjack tuna migration in the western North

Pacific, followed by SSC.

The oligotrophic side of the Kuroshio Front and the

Kuroshio Extension were important skipjack tuna

habitat features. Meso-scale features such as eddies played a role

in formation of skipjack tuna habitat, although

that role may not be as profound as that of SST and

SSC.

Our work was based on 1-yr fishery and remotely

sensed environmental data. Future work based on

longer term fishery data sets, with environment data at

higher temporal and spatial resolutions could further

improve skipjack tuna habitat models.

ACKNOWLEDGEMENTS

We are greatly indebted to one anonymous reviewer

for providing useful comments on earlier versions of

this manuscript. We also appreciate Mr. Fumihiro

Takahashis advice on some aspects of satellite image

analyses. We also appreciate the use of AQUA-MODIS

SST and chlorophyll-adata sets, downloaded from thePhysical Oceanography Distributed Active Archive

Center (PODAAC) at the Jet Propulsion Laboratory

(JPL) (http://podaac.jpl.nasa.gov) and the ocean color

portal (http://oceancolor.gsfc.nasa.gov), respectively.

394 R. Mugoet al.



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Use of SSHA and geostrophic velocities data distrib-

uted by AVISO is also acknowledged.

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