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Bigeye tuna (Thunnus obesus) is one of the commercially important pelagic species that caught mostly in the eastern Indian Ocean. This species prefers to stay close, and is usually below the thermocline layer. Remotely sensed data was used to determine the characteristics of Bigeye tuna fishing areas at a depth of 155 meter. Fishing vessels for Bigeye tuna were obtained from vessel monitoring systems (VMS) from January through December, 2015-2016. Daily data on sub-surface temperature (SST), sub-surface chlorophyll-a concentration (SSC), and sub-surface salinity (SSS) were obtained from the INDESO Project website. All oceanographic parameter data were selected at a depth of 155 m. The position of Bigeye tuna and oceanographic data were then grouped into 2 group monsoon, southeast monsoon (April – September) and northwest monsoon (October – March). The results showed that, during the southeast and northwest monsoon, Bigeye tuna mostly found in SSC of 0.03 – 0.05 mg/m3, SST of 16° - 18°C and salinity of 34 psu. These results showed that at depth of 155 m, Bigeye Tuna prefers to stay in small chl-a (0.03 – 0.04 mg/m3), low SST (16° - 18°C) and salinity of 34 psu. These information were essential and could be used to support fisheries management decisions especially for Bigeye Tuna in the eastern Indian Ocean.

Remotely sensed data and habitat model approach were employed to evaluate the present of oceanographic aspect in the Bigeye tuna's potential fishing zone (PFZ) at a profundity of 155 m. Vessel monitoring system was employed to acquire the angling vessels for Bigeye tuna from January through December, 2015-2016. Daily data of sub-surface temperature (Sub_ST), sub-surface chlorophyll-a (Sub_SC), and sub-surface salinity (Sub_SS) were downloaded from INDESO Project website. Vessel monitoring system and environmental data were employed for maximum entropy (maxent) model development. The model predictive achievement was then estimated applying the area under the curve (AUC) value. Maxent model results (AUC>0.745) exhibited its probable to understand the Bigeye tuna's spatial dispersion on the specific sub-surface. In addition, the results also showed Sub_ST (43,1%) was the most affective aspect in the Bigeye tuna dispersion, pursued by Sub_SC (35,2%) and Sub_SS (21,6%).

Tide and tidal current model of the Bali Strait in Indonesia is produced by using a Coupled Hydrodynamical-Ecological Model for Regional and Shelf Seas (COHERENS). With its resolutions in the horizontal (500meters) and the vertical (4layers), the model well reproduces the four major tidal constituents, namely M2, S2, K1, and O1 tides, and their currents. Furthermore the model is used to investigate the tide-induced residual flow and tidal front in the Bali Strait. As a results, the tide-induced residual flow in the Bali Strait during the spring tide on May 16th in 2010 can be attributed to the variation of the strength of two eddies. The first one is the clockwise circulation in the shallow area at the wide part of the strait, while the second one is the small clockwise circulation in the south of the narrow strait. On the other hand, as suggestion from Simpson and Hunter (1974), the tidal front is determined by the value of log(H/U3) (where is the water depth in meters and the amplitude oftidal current amplitude in ms-1). The front detected by the image of sea surface temperature distribution from the satellite corresponds with the contour log(H/U3) of 6.5.

Bigeye tuna (Thunnus obesus) is one of the commercially important fish in the eastern Indian Ocean and is a highly migratory species. A modeling approach and remotely sensed data were used to develop an appropriate prediction model and to understand the contribution of oceanographic factors in the distribution of Bigeye tuna in 2 different depths. The daily data of sub-surface chlorophyll-a (SSC), sub-surface temperature (SST) and sub-surface salinity (SSS) were downloaded from infrastructure development of space oceanography (INDESO) project website, meanwhile fishing vessel for Bigeye tuna were obtained from vessel monitoring system (VMS) from April through September 2015 - 2016. The daily VMS data and environmental factors were used for maximum entropy model construction. The predictive model performance was then assessed using a threshold-independent metric, the area under the curve (AUC) metric of the receiver operating characteristic (ROC). Maximum entropy model results based on AUC (more than 0.80) indicated its potential to deduce the spatial distribution of Bigeye tuna. At a depth of 155 m, SSC (37.9%) is the most effective variable in the Bigeye tuna distribution, followed by SST (32.7 %) and SSS (29.4%), meanwhile at a depth of 222 m, SSS (46.8%) is the most important variable followed by SST (31.3%) and SSC (21.9%). Combination of a modelling approach and multi-sensor remote sensing data offers an innovative way to determine the potential fishing zone of the Bigeye tuna in the eastern Indian Ocean.

In the framework of the INDESO (Infrastructure Development of Space Oceanography) project, an operational ocean forecasting system was developed to monitor the state of the Indonesian seas in terms of circulation, biogeochemistry and fisheries. This forecasting system combines a suite of numerical models connecting physical and biogeochemical variables to population dynamics of large marine predators (tunas). The physical–biogeochemical coupled component (the INDO12BIO configuration) covers a large region extending from the western Pacific Ocean to the eastern Indian Ocean at 1/12° horizontal resolution. The NEMO-OPA (Nucleus for European Model of the Ocean) physical ocean model and the PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies) biogeochemical model are running simultaneously ("online" coupling), at the same resolution. The operational global ocean forecasting system (1/4°) operated by Mercator Océan provides the physical forcing, while climatological open boundary conditions are prescribed for the biogeochemistry. This paper describes the skill assessment of the INDO12BIO configuration. Model skill is assessed by evaluating a reference hindcast simulation covering the last 8 years (2007–2014). Model results are compared to satellite, climatological and in situ observations. Diagnostics are performed on nutrients, oxygen, chlorophyll a, net primary production and mesozooplankton. The model reproduces large-scale distributions of nutrients, oxygen, chlorophyll a, net primary production and mesozooplankton biomasses. Modelled vertical distributions of nutrients and oxygen are comparable to in situ data sets although gradients are slightly smoothed. The model simulates realistic biogeochemical characteristics of North Pacific tropical waters entering in the archipelago. Hydrodynamic transformation of water masses across the Indonesian archipelago allows for conserving nitrate and oxygen vertical distribution close to observations, in the Banda Sea and at the exit of the archipelago. While the model overestimates the mean surface chlorophyll a, the seasonal cycle is in phase with satellite estimations, with higher chlorophyll a concentrations in the southern part of the archipelago during the SE monsoon and in the northern part during the NW monsoon. The time series of chlorophyll a anomalies suggests that meteorological and ocean physical processes that drive the interannual variability of biogeochemical properties in the Indonesian region are reproduced by the model.