Habitat Model Development of Bigeye tuna (Thunnus obesus) during Southeast Monsoon in the Eastern Indian Ocean using Satellite Remotely Sensed Data

• Published in: IOP conference series. Earth and environmental science Vol. 276; no. 1; pp. 12011 - 12019
• Main Authors: Fachruddin Syah, Achmad, Lumban Gaol, Jonson, Zainuddin, Mukti, Apriliya, Nadela R., Berlianty, Dessy, Mahabror, Dendy
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2019
• Subjects: Bigeye tuna; Chlorophyll; different depths; eastern Indian Ocean; Entropy; Environmental factors; Fishing; Fishing vessels; Fishing zones; Indian spacecraft; Maximum entropy; maximum entropy model; Migratory species; Monitoring systems; Ocean models; Oceanography; Performance prediction; potential fishing zones; Prediction models; Remote sensing; Remote sensors; Sea surface temperature; Spatial distribution; Thunnus obesus; Tuna; Websites; Indian Ocean
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/276/1/012011

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.