USING SATELLITE REMOTE SENSING DATA TO IDENTIFY DISTRIBUTION AND
MIGRATION PATTERNS FOR SKIPJACK TUNA IN THE BONE BAY-FLORES SEA
Mukti Zainuddin1, M. Banda Selamat1, St. Aisjah Farhum1, Safruddin1
1
Fakultas Ilmu Kelautan dan Perikanan, UNHAS
Email: mukti_fishocean@yahoo.co.id
Abstract
Bone Bay-Flores Sea is one of the most potential tuna fishing grounds in the eastern part of Indonesia. However,
their potential spatial distribution and migration patterns in that area are still unclear. The objectives of this study
are to assess the hotspot habitat and to analyze the distribution and migration patterns for skipjack tuna throughout
the study area. To meet these objectives, we combined satellite data of sea surface temperature (SST) and surface
chlorophyll-a (Chl-a) together with catch data. The hotspot habitat was identified using habitat suitability index
analysis. The fish distribution and migration patterns were analyzed using the dynamics spatial of the habitat for
January-December. Our results indicated that the ocean hotspot developed mostly in August over the eastern part of
Bone Bay which could be identified by SST of 29.5-31.5°C and Chl-a of 0.15-0.25 mg m-3. For the period of study,
the skipjack tuna showed clockwise migration pattern and corresponded to the hotspots distribuiton. These findings
suggested that the potential tuna habitat may correspond to the transient oceanographic structures such as front
and upwelling found in the Flores Sea.
Keywords: habitat hotspot, skipjack tuna, preferred oceanographic structures, satellite images, migration pattern
1. Introduction
Skipjack tuna (Katsuwonus pelamis) is one of the most important species targeting by local fishermen in
the Bay of Bone-Flores Sea, South Sulawesi. The distribution and abundance of this species are strongly
influenced by oceanographic factors such as distributions of SST and chlorophyll-a concentration
(hereafter Chl-a) (Zainuddin et al., 2013). Skipjack migration route and habitat in the Bone Bay clearly
correspond to the movement of optimum range SST (Zainuddin., 2011) . The distribution of the 29°C
SST isotherm is a reasonable proxy to detect the region of highest skipjack CPUEs (frontal area) in
western Pacific Ocean (Lehodey et al., 1997). The highest skipjack CPUEs off the southern Brazilian
coast occur in waters of SST 22°-26.5°C, although that relationship varied seasonally (Andrade and
Garcia, 1999).
An integration of satellite remote sensing (SRS) and geographic information system (GIS)
provides a powerful tool to detect potentially marine fish habitat particularly for highly migratory tuna.
Significant information of oceanographic conditions of SST and Chl-a can be derived continuously and
systematically in a wide area coverage. This information will be important to explore potential fishing
ground and migration route of tuna. SST and Chl-a derived from satellite data are determinant factors in
predicting tuna forage and their habitat in Western North Pacific Ocean (Polovina et al., 2001;Mugo et al.,
2010). Therefore, the objectives of this study are to explore the hotspot habitat and to analyze the
distribution and migration patterns for skipjack tuna throughout the study area pattern using SRS data and
GIS techniques.
2. Data and Methods
The satellite-basedl environmental data used to describe the oceanographic conditions at the fishing
ground were SST and Chl-a data derived from Terra/MODIS. The NASA distributes Standad Mapped
Image
(SMI)
level
3
binary
data
with
HDF
(Hierarchical
Data
Format)
format
(http://oceancolor.gsfc.nasa.gov/cms/). We used the satellite data with monthly temporal resolution,
June-December 2014 and January-May 2015, and 0.044° of longitude and latitude spatial resolution. We
processed the data using
IDL (Interactive Data Language) software package to get image data
throughout the study area. The pole and line fishery data consisted of fishing ground position in latitude
and longitude, SST fishing boat and daily CPUE data. We compiled the daily data into monthly to match
with the satellite data temporal resolution. The oceanographic data were linked to fishery data to estimate
and extract SST and Chl-a corresponding to the fishing ground locations.
In this study, firstly we plotted the fishing data on SST and Chl-a image map to understand the
spatial and temporal distribution pattern of skipjack fishing grounds relative to the oceanographic
conditions. Using gravity center of fishing ground, we then investigated the center of fishing ground
position for the period of one year. . The distribution pattern of skipjack was determined by computing
the latitudinal and longitudinal gravity center of fishing ground according to Lehodey et al. (1997).
Secondly, we mapped out the hotspot of skipjack, i.e. the area where skipjack tuna schools were mostly
abundance in relation to specific oceanographic features (Zainuddin et al., 2006). We considered that the
movement of the hotspot within the potential fishing zone map every month reflecting the movement of
skipjack concentration in average which in turn describe the migration pattern of skipjack tuna in the
study area. Thirdly, we applied the kinesis model for skipjack tuna movement and investigate the
migration pattern for the fish throughout the study area every month from January to December. All
images produced in this study were mapped using ArcGIS 10 and Generic Mapping Tools (GMT)
software packages.
3. Results and Discussion
In the Bone Bay, the most PFZs for skipjack tuna were found in May with area coverage of 34706.994
km2 and were consistent with the highest CPUE obtained by the fishermen (Fig. 1). In this month, the
mean probability index (84.520%) of finding fish was highest. The PFZs in May were located in the
region of 4-4.5°S and 120.5-121.7°E. In January, the PFZs for skipjack covered the widest area of 54641
km2. Within the Bone Bay, the PFZs well formed and developed in January and May. Whilst, in the
Flores Sea, the PFZs were clearly detected from February to April, and reached the maximum probability
in April. In Makassar Strait, the PFZs mainly appeared in January, May and June.
Figure 1. The spatial distribution of skipjack CPUE (skipjack/fishing trip) from the pole and line fishery
shown as dots for January-June overlain on overlain on hotspots (the probability index) maps
of finding skipjack.
In January and May, the PFZs for skipjack well develop in Bone Bay. The potential habitats may
create a good feeding opportunity for skipjack which probably stimulates by the frontal zones. In this
study, we identified the most PFZs for skipjack tuna by using 30.5° C SST isotherm and 0.2 mg m-3 Chl-a
isopleths. Several studies identify tuna forage habitat in different areas using proxy environmental
variables such as the specific levels of SST isotherm and surface chlorophyll-a isopleths (Lehodey at al.,
1997; Polovina et al., 2001; Zainuddin et al., 2004). During February-April, the PFZs were mostly found
in the Flores Sea. The PFZs in this area during the period may drive by the existing upwelling area
(Gordon, 2005) and the high productivity area near coral reef ecosystem of Takabonerate Islands.
Figure 2 showed that there were two main groups of gravity center of fishing ground for skipjack
tuna in the Bone Bay-Flores, northern and southern ones. Specifically, in the Bone Bay, the center
fishing ground was located in area of 3-4°S latitude and 120.5-121°E longitude.
Whereas, in the Flores
Sea, the hotspot area was located in the waters of about 6°S latitude and 120.5-121°E longitude of the
eastern part of Selayar Island. The high productive habitat mostly developed in the southeast monsoon
(April-August).
Figure 2. Map of gravity center of fishing ground distribution for the period of JanuaryDecember in the Bone Bay-Flores Sea.
The frequency of Catch Per Unit Effort (CPUE) (fish/fishing set) in relation to both SST and
Chl-a derived from satellites showed that there are specific ranges where skipjack tend to concentrate
(Fig.3). Skipjack fishing grounds occurred in areas and periods where SST ranged from 28 to 32°C and
Chl-a concentration ranged from 0.15 to 0.60 mg m-3. However, most of the catches were obtained in
waters where SST and Chl-a varied from 30 to 31°C and from 0.15 to 0.35 mg m-3, respectively. These
results are similar to the previous study in the same area (Zainuddin, 2011). These areas were well known
as hotspots for skipjack. Our preliminary study found that the relationship between SST fishing boat and
SST satellite data was statistically significant (P <0.0001, n=460). Therefore, we used the SST satellite
data together with Chl-a as input data on the migration model to generate distribution and migration
patterns for skipjack in the Bay of Bone and Flores Sea.
Figure 3. The distribution of total skipjack CPUE (skipjack/trip) in relation to MODIS SST (left) and
MODIS chlorophyll-a (right) during January-June.
It is inetersting to note that distribution and migration pattern for skipjack tuna in the Bone BayFlores Sea have any relationship with the hotspot distribution pattern. Most of the fish produced by
simulation occurred within the hotspot location (Figure 4). This figure described that the center of fishing
ground and hotspot are mainly in Kolaka and Luwu waters. In Juli the fish schools concentrated in the
areas of southeast Bone Bay which appeared to be consistent with hostpot locations. In the Flores, during
this month hotspots were not well enhanced. Therefore, the fishermen should increase the fishing efforts
in the hotspot areas of Bone Bay for this month.
In August, the simulated fish concentrated in a specific location of southeastern Bone Bay near
Kolaka coastal waters. This period indicates the peak season of skipjack tuna in the Bone Bay. This result
was strengthen by fishing data (Zainuddin et al., 2013). The increasing CPUEs in this month as a result
of increasing nutrient rich-water over the hotspot area. This may be caused the upwelling and frontal
system operating in the Flores Sea and tranfort the highly nutrient stimulating a good feeding opportunity
in the hotdpot areas of Bone Bay. Therefore, this study suggests that the pole and line fishing efforts in
this month should be increased to get the high CPUE for skipjack.
Figure 4. The Spatial Distribution of Potential Fishing Zones (Skipjack/Fishing-sets) for Skipjack tuna
Produced By migration Model superimposed on hotspot map for July-August 2014.
4. Conclusions
Using satellite remote sensing and catch data, the distribution and migration patterns for skipjack were
closely related to the spatial distribution of the optimum value of SST and Chl-a ranges (hotspot areas).
Our results indicated that the movement of the fish schools show clock wise migration pattern. This
study illustrates that using satellite remote sensing data, distribution and migration of tuna could be
identified.
Acknowledgement
We are greatly indebted to Rini Sahni Putri dan Nur Indah Reskyanti for all assistances in collecting the
pole and line fishery data in Bone Bay and Flores Sea. We also appreciate the use of AQUA-MODIS SST
and chlorophyll-a data sets, downloaded from the ocean color portal (http://oceancolor.gsfc.nasa.gov).
This work was supported to MZ. by the National Competitive Research Grant (Competence Grant 2015),
Directorate General of Higher Education.
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