Operational modelling of bigeye tuna (Thunnus obesus) spatial dynamics in the Indonesian region. (June 2018)

• Record Type: Journal Article
• Authors: Lehodey, P.; Senina, I.; Wibawa, T.A.; Titaud, O.; Calmettes, B.; Conchon, A.; Tranchant, B.; Gaspar, P.
• Is Part Of: Marine pollution bulletin. Volume 131(2018)Part B
• Page Start: 19
• Page End: 32
• Publication Date: 2018-06
• Subjects: Bigeye tuna -- Ecosystem modelling -- Maximum Likelihood Estimation -- Operational oceanography -- SEAPODYM -- Tuna fisheries; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2017.08.020
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: With INDESO, Indonesia has implemented a system for the monitoring and management of its tuna resources. Despite increasing catch, very few is known about the dynamics and real abundance of tuna species in the Indonesian waters and adjacent oceanic regions. The SEAPODYM model was implemented in an operational chain of production for the Indonesian region to simulate tuna spatial dynamics in realtime. This challenging objective imposed developing a global scale model at coarse resolution to provide initial and boundaries conditions of the regional model. A parameter optimization approach was used to provide the best solution fitting several hundreds of thousand catch observations, over a long historical simulation at coarse resolution. Then downscaling method and regional modelling at high resolution (1/12°x day) were validated to produce realtime and forecast on a weekly basis. The architecture of this application, the approach for its parameterization and some key results are presented and discussed. Highlights: Indonesia has implemented a new challenging system for the monitoring and management of its tuna resources. The system uses the model SEAPODYM in an operational chain of production to provide forecast distributions of tuna density by life stages. A global scale model at coarse resolution had to be developed to provide initial and boundaries conditions of the regional model. The model is parameterized using a maximum likelihood estimation approach fitting several hundreds of thousand catch observations. Downscaling method and regional modeling at high resolution (1/12°x day) were validated to produce realtime and forecast on a weekly basis.