Effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean off Java

Syamsuddin, Mega L.; Saitoh, Sei–Ichi; Hirawake, Toru; Bachri, Samsul; Harto, Agung Budi

doi:10.7755/fb.111.2.5

Cited by 50 publications

(30 citation statements)

References 38 publications

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“…In other words, bigeye tuna prefer to remain in the low SST around the study area. This result displays the same trends as those observed with the GAM analysis in the same study area [21,25]. The optimum value can be inferred by determining the threshold (i.e., average number of bigeye tuna).…”

Section: Relationship Between Environmental Factors and Bigeye Tuna Csupporting

confidence: 79%

“…Every year, the higher concentrations of SSC occur in June to September and lower concentrations occur in December, January, and February [37,38]. These conditions directly affect the amount of fishing catches [21].…”

Section: Fisheries Data and Remotely Sensed Environmental Datamentioning

confidence: 99%

“…Even though fishery data is not free from bias, it represents a low-cost source of data for fish distribution which available to scientists [27]. 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.…”

Section: Relationship Between Ocean Dynamics and Preferred Habitat Fomentioning

confidence: 99%

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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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Section: Relationship Between Environmental Factors and Bigeye Tuna Csupporting

confidence: 79%

Section: Fisheries Data and Remotely Sensed Environmental Datamentioning

confidence: 99%

Section: Relationship Between Ocean Dynamics and Preferred Habitat Fomentioning

confidence: 99%

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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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“…Migration over 1,000 km mostly is involved by seasonal migration (Cosgrove et al, 2014). Tuna seasonal migration is caused by seasonal oscillation (El Nino and La Nina) characterized by a difference sea surface temperature (Syamsuddin et al, 2013). Vertically, ALB tuna can move to seek the preferred area.…”

Section: Swimming Layer and Oceanographic Parametersmentioning

confidence: 99%

THE INFLUENCE OF SWIMMING LAYER AND SUB-SURFACE OCEANOGRAPHIC VARIABLES ON CATCH OF ALBACORE (Thunnus alalunga) IN EASTERN INDIAN OCEAN

Rochman

Pranowo

Jatmiko

2017

Indonesian Fisheries Research Journal

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This study was highlighted the contenxt of albacore's number catch, swimming layer and subsurface oceanographic variables (SSOV) at Eastern Indian Ocean include temperature, dissolved oxygen, salinity, nitrate, phosphate and silicate. Hopefully the information would be useful for the longliners to understand the ALB behaviour, environment and the best techniques on how to catch this fish. Data in this study were based on the Research Institute for Tuna Fisheries (RITF) observer program in Benoa from 2010-2013. Data analysis was base on primary data and secondary data. Primary data are albacore's (ALB) swimming layer data which are measured by minilogger. Secondary data is SSOV data which extracted from World Ocean Atlas 2009 (WOA09). The results show that the optimum catch of albacore occurred at depth of 118 to 291 m with the average temperature between 12.41-20.47°C, dissolved oxygen 3.24-4.68 ml/l , salinity 34.78-35.01 psu, nitrate 6.78-17.50 µ mol/ l, phosphate 0.62-1.27 µ mol/l and silicate 10.06-24.77 µ mol/l. The highest catch of ALB was mostly at depth of 156 m (hook number 2 and 11) with the average temperature 18.71°C, dissolved oxygen 4.68 ml/l, salinity 34.78 psu, nitrate 10.71 µ mol/l, phosphate 0.86 µ mol/l and silicate 15.95 µ mol/l. The highest influence of swimming layer and sub-surface oceanographic variable to the number of ALB catch happened at depth of 291 m of ALB swimming layer with coefficient correlation (r) of 0.934 and determination coefficient (R 2 ) of 0.872. The lowest influence of swimming layer and sub-surface oceanographic variable to the number of ALB catch happened at depth of 156 m of albacore swimming layer with coefficient correlation (r) of 0.528 and determination coefficient (R 2 ) of 0.279.The relationship between swimming layer and sub-surface oceanographic variable on catch of ALB tuna was low (<0.500).

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“…In the eastern part of the Indian Ocean, especially in the south waters of Java, big eye tuna was the main commercial targets in fishing operation (M.L. Syamsuddin, 2013).…”

Section: Introductionmentioning

confidence: 99%

The Spatial Analysis in Tuna Habitat Related to The Ocean Variability in The Indian Ocean

Sambah¹,

Iranawati²,

Julindasari³

et al. 2017

Proceedings of the LST International Cohference on Geography and Education (ICGE 2016)

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Advances in Social Science, Education and Humanities Research, volume 79262 baby) was 17.092 kg on September in the coordinate of 108.83° E and 08.50° S, and big eye baby tuna was 7.896 kg on January in the coordinate of 107.00° E and 08.00° S. Bigeye tuna is more dominant caught than big eye baby tuna in Southern Java between 105.00° to 110.00° E longitude and 08.00° to 10.00° S latitude, and slightly in Southwestern of Java. Based on the distribution of big eye tuna fishing ground in the Indian Ocean described that big eye tuna were caught in the range of SST, chlorophyll-a, and SSHA of 25-30°C, 0.10-0.71 mg/m3 and -0.14-0.16 m. The accuracy assessment of tuna fishing ground during 2015 by local fisherman associated with the prediction of potential fishing ground showed 78,82 % of accuracy.

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Effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean off Java

Cited by 50 publications

References 38 publications

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

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

THE INFLUENCE OF SWIMMING LAYER AND SUB-SURFACE OCEANOGRAPHIC VARIABLES ON CATCH OF ALBACORE (Thunnus alalunga) IN EASTERN INDIAN OCEAN

The Spatial Analysis in Tuna Habitat Related to The Ocean Variability in The Indian Ocean

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