Interannual variation of bigeye tuna (<i>Thunnus obesus</i>) hotspots in the eastern Indian Ocean off Java

Syamsuddin, Mega L.; Saitoh, Sei–Ichi; Hirawake, Toru; Syamsudin, Fadli; Zainuddin, Mukti

doi:10.1080/01431161.2015.1136451

Cited by 22 publications

(20 citation statements)

References 29 publications

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“…Albacore forage habitat and migration routes are driven by the dynamic features of a pelagic hotspot namely a. Chl-a front known as the TZCF in the eastern and central Pacific Ocean [ 9 ]. Recent findings suggest that the frontal area, eddy field, and topographic features (seamount) are important habitat hotspots for pelagic species such as flying squid and tuna [ 10 , 11 , 12 ]. Therefore, detection of the ecologically significant pelagic habitats and their spatial persistence is critical for marine management strategies and identifying potential targets for conservation.…”

Section: Introductionmentioning

confidence: 99%

Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia

et al. 2017

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Using remote sensing of sea surface temperature (SST), sea surface height anomaly (SSHA) and chlorophyll-a (Chl-a) together with catch data, we investigated the detection and persistence of important pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, Indonesia. We analyzed the data for the period between the northwest and southeast monsoon 2007–2011. A pelagic hotspot index was constructed from a model of multi-spectrum satellite-based oceanographic data in relation to skipjack fishing performance. Results showed that skipjack catch per unit efforts (CPUEs) increased significantly in areas of highest pelagic hotspot indices. The distribution and dynamics of habitat hotspots were detected by the synoptic measurements of SST, SSHA and Chl-a ranging from 29.5° to 31.5°C, from 2.5 to 12.5 cm and from 0.15 to 0.35 mg m-3, respectively. Total area of hotspots consistently peaked in May. Validation of skipjack CPUE predicted by our model against observed data from 2012 was highly significant. The key pelagic habitat corresponded with the Chl-a front, which could be related to the areas of relatively high prey abundance (enhanced feeding opportunity) for skipjack. We found that the area and persistence of the potential skipjack habitat hotspots for the 5 years were clearly identified by the 0.2 mg m-3 Chl-a isopleth, suggesting that the Chl-a front provides a key oceanographic indicator for global understanding on skipjack tuna habitat hotspots in the western tropical Pacific Ocean, especially within Coral Triangle tuna.

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Section: Introductionmentioning

confidence: 99%

Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia

et al. 2017

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“…Thermal or Chl-a fronts often indicate areas of high biological productivity, and hence a high probability finding fish. Primary production provides an attractive habitat for small pelagic fish species [4,14]. The SSHA and SST were the second and third significant predictor of Eastern Little Tuna.…”

Section: Fishing Ground Modeling Of Gammentioning

confidence: 99%

Oceanographic factors related to Eastern Little Tuna (Euthynnus affinis) catches in the west Java Sea

Syamsuddin

Sunarto

Yuliadi

2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. The satellite data included sea surface height anomaly (SSHA) from AVISO, sea surface temperature (SST) and chlorophyll-a (Chl-a) from Aqua MODIS and Eastern Little Tuna (Euthynnus affinis) catches were used as a combined dataset to understand the ocean variations and further addresses their relations with the Eastern Little Tuna catches in the Java Sea. The fish catches and remotely sensed data were analyzed for the 5 years datasets from 2010-2014. The relationships of oceanographic factors and catch distribution were explored with a generalized additive model (GAM). Catch rates varied temporally relatively significant over year-round. The Eastern Little Tuna catch rates have the peak season in March and October (900 ton to 1000 ton). The ELT catch peaks during the transition season from southeast to northwest monsoon (September to November) and decreases during southeast monsoon (June to August). The GAM results showed that the 3-oceanographic parameter combination models explained the highest deviance (41.4%) with Chl-a explained the highest deviance (23.3%). High probabilities of Eastern Little Tuna catches corresponded to marine productivity of Chl-a concentration ranging from 0.3-0.5 mg/m 3 , for SSHA ranging from 0-8 cm and SST ranging from 28-29°C. We recommend to have further investigations through the use of long-term historical time series to predict fishing ground locations and an emerging need to improve our climate understanding and forecast skill to conserve small pelagic catches.

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“…SSHD also influenced bigeye tuna behavior, because bigeye tuna migration is influenced by the thermocline layer and it can be measured by calculating SSHD [21]. According to Syamsuddin [45], "extreme negative SSHD gave the positive effect to bigeye tuna" because it made the thermocline layer closer to the surface. In this study, extreme negative SSHD resulted in a positive effect in regards to the number of bigeye tuna (Figure 10), but it had high standard error.…”

Section: Relationship Between Ocean Dynamics and Preferred Habitat Fomentioning

confidence: 99%

Utilization of Scatterplot Smoothers to Understand the Environmental Preference of Bigeye Tuna in the Southern Waters off Java-Bali: Satellite Remote Sensing Approach

Setiawati

Tanaka

2017

Fishes

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Abstract:The southern waters off Java-Bali were recognized as spawning and potential fishing ground for tuna species. However, few studies have been conducted on this area. In this paper, the environmental preference of bigeye tuna was assessed based on catch data and three main environmental satellite data; namely; sea surface temperature (SST), sea surface chlorophyll (SSC), and sea surface height deviation (SSHD). Then, the relationship between bigeye tuna catches and environmental satellite data was analyzed by using a simplified method of the Generalized Additive Model (GAM) which is called scatterplot smoothers. This method is the forerunner of GAM and has not yet been applied for fisheries analysis. The aim of this study was to evaluate its performance for/in analyzing bigeye tuna habitat preference. The result indicated that SST, SSC, and SSHD had a high correlation with the bigeye tuna's spatial patterns. Furthermore, spatial patterns of bigeye tuna preference display typical characteristics of low SST, low SSC, and low positive SSHD as well as areas with extreme SSHD values, which are almost the same results as those identified with GAM analysis in the same study area.

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Interannual variation of bigeye tuna (Thunnus obesus) hotspots in the eastern Indian Ocean off Java

Cited by 22 publications

References 29 publications

Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia

Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia

Oceanographic factors related to Eastern Little Tuna (Euthynnus affinis) catches in the west Java Sea

Utilization of Scatterplot Smoothers to Understand the Environmental Preference of Bigeye Tuna in the Southern Waters off Java-Bali: Satellite Remote Sensing Approach

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