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Dortel Emmanuelle, Sardenne Fany, Bousquet N., Rivot E., Million J., Le Croizier G., Chassot Emmanuel. (2015). An integrated Bayesian modeling approach for the growth of Indian Ocean yellowfin tuna. In : Murua H. (ed.), Marsac Francis (ed.), Eveson J.P. (ed.) IO Tuna tagging. Fisheries Research, 163 (SI), 69-84. ISSN 0165-7836

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Lien direct chez l'éditeur doi:10.1016/j.fishres.2014.07.006

An integrated Bayesian modeling approach for the growth of Indian Ocean yellowfin tuna
Année de publication2015
Type de documentArticle référencé dans le Web of Science WOS:000348971300007
AuteursDortel Emmanuelle, Sardenne Fany, Bousquet N., Rivot E., Million J., Le Croizier G., Chassot Emmanuel.
InMurua H. (ed.), Marsac Francis (ed.), Eveson J.P. (ed.) IO Tuna tagging
SourceFisheries Research, 2015, 163 (SI), p. 69-84. ISSN 0165-7836
RésuméThe Indian Ocean Tuna Tagging Program provided a unique opportunity to collect demographic data on the key commercially targeted tropical tuna species in the Indian Ocean. In this paper, we focused on estimating growth rates for one of these species, yellowfin (Thunnus albacares). Whilst most growth studies only draw on one data source, in this study we use a range of data sources: individual growth rates derived from yellowfin that were tagged and recaptured, direct age estimates obtained through otolith readings, and length-frequency data collected from the purse seine fishery between 2000 and 2010. To combine these data sources, we used an integrated Bayesian model that allowed us to account for the process and measurement errors associated with each data set. Our results indicate that the gradual addition of each data type improved the model's parameter estimations. The Bayesian framework was useful, as it allowed us to account for uncertainties associated with age estimates and to provide additional information on some parameters (e.g., asymptotic length). Our results support the existence of a complex growth pattern for Indian Ocean yellowfin, with two distinct growth phases between the immature and mature life stages. Such complex growth patterns, however, require additional information on absolute age of fish and transition rates between growth stanzas. This type of information is not available from the data. We suggest that bioenergetic models may address this current data gap. This modeling approach explicitly considers the allocation of metabolic energy in tuna and may offer a way to understand the underlying mechanisms that drive the observed growth patterns.
Plan de classementRessources halieutiques [040] ; Sciences fondamentales / Techniques d'analyse et de recherche [020]
LocalisationFonds IRD [F B010063905]
Identifiant IRDfdi:010063905
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010063905

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