Empirical Cumulative Distribution Function (ECDF) Analysis of Thunnus.sp Using ARGO Float Sub-surface Multilayer Temperature Data in Indian Ocean South of Java

Abstract

Spatial distribution of tuna (Thunnus.sp) in Indian Ocean south of Java has been investigated. Tuna was scientifically known as thermo-conformer species, thus their distribution were strongly influenced by sub-surface temperature. Tuna species in this study comprise of bigeye tuna (Thunnus obesus), albacore tuna (Thunnus alalunga), yellowfin tuna (Thunnus albacares) and southern bluefin tuna (Thunnus maccoyii). The study was conducted in the area between 100 o E -127 o E and 7 o S -20 o S during 2013 covering of southeast monsoon (April -September) and northwest monsoon (October -March) data. About 1200 coordinate of ARGO Float data and actual catch of tuna from fishing fleet in the same day were processed to obtain the polynomial equation and correlation coefficient. ARGO Float data were processed using kriging method. Correlation coefficient method that used in the study was Empirical Cumulative Distribution Function (ECDF), while spatial distribution equation was developed by polynomial regression equation. Sub-surface temperature in Indian Ocean south of Java fluctuates seasonally. Temporal distribution of dataset indicates that sub-surface temperature was warmer in northwest monsoon than in southeast monsoon. Seasonal fluctuation of sub-surface temperature may vary due to occurrence of upwelling. T. alalunga, T. Albacares and T. Obesus were found to be more favour in the depth around 150m with optimum temperature between 16 o C -21 o C, while T. maccoyii were found in the dept around 250m with optimum temperature between 13 o C -16 o C. Potential fishing zone for Thunnus.sp in southeast monsoon was wider than in northwest monsoon. This condition was according to seasonal variability of sub-surface temperature.

Detection of pelagic habitats and abundance of skipjack tuna in relation to the environment in the Indian Ocean around Sri Lanka

“…To describe the relationship between oceanographic conditions of SST, Chl-a and fish CPUE, empirical cumulative distribution function (ECDF) analysis was used. The ECDF functions (Sukresno et al 2015) can be mathematically represent as follows:…”

Role of Sub Surface Temperature, Salinity and Chlorophyll to Albacore Tuna Abundance in Indian Ocean

“…Qu et al (2005), stated that sub-surface temperature in the Indian Ocean was influenced by monsoon. In addition temporal distribution of sub-surface temperature in Indian Ocean part South of Java was warmer in northwest monsoon than in southeast monsoon (Sukresno et al, 2014;Hartoko, 2010). The current study showed that ALB catches varied in the temperature range with the highest frequency at 24.0-24.9 0 C. Different result from Sukresno et al (2014), in the same region concluded ALB were found with optimum temperature between 17 -21 0 C, where ALB avoids waters below 14 ºC (Arrizabalaga et al, 2015).…”

“…In addition temporal distribution of sub-surface temperature in Indian Ocean part South of Java was warmer in northwest monsoon than in southeast monsoon (Sukresno et al, 2014;Hartoko, 2010). The current study showed that ALB catches varied in the temperature range with the highest frequency at 24.0-24.9 0 C. Different result from Sukresno et al (2014), in the same region concluded ALB were found with optimum temperature between 17 -21 0 C, where ALB avoids waters below 14 ºC (Arrizabalaga et al, 2015). Meanwhile Zainuddin et al (2011), in the western North Pacific Ocean indicated that the highest concentrations of ALB were found in water of 18.5 -21.5 ºC and tended to be centered at 20 ºC.…”

“…Vol.22 No.1 June 2016:17-26 the most productive fishing grounds and has a long history of tuna exploitation, little is known about the oceanographic conditions affecting the distribution and abundance of ALB in those waters. Previous studies showed that environmental factor is the most important parameter ALB abundance in the Indian Ocean (Lan et al, 2012;Chen et al, 2005, Sukresno et al, 2014, Arrizabalaga et al, 2015, but the related literature of ALB in the Indian Ocean is scarce in comparison with other oceanic basins like the Atlantic and Pacific Oceans.…”

Pemanfaatan Data Satelit Oseanografi Untuk Memprediksi Daerah Penangkapan Ikan Lemuru Berbasis Rantai Makanan Dan Pendekatan Statistik Gam

“…Namun demikian, umumnya hubungan antara kelimpahan sumberdaya perikanan dengan dinamika faktor lingkungannya tidak linear sehingga pendekatan statistik lebih berkembang dan banyak digunakan (Zuur et al, 2009). Sukresno et al, (2015) memprediksi sebaran spasial tuna di Samudera Hindia menggunakan pendekatan statistik polinomial regresi berdasarkan parameter suhu perairan. Dari berbagai pendekatan statistik yang ada, generalized additive model (GAM) lebih banyak digunakan untuk pemodelan habitat suatu jenis ikan karena lebih mampu mengakomodasi hubungan yang tidak linear tersebut (Valavanis et al, 2008).…”