Standardisation of yellowfin tuna CPUE for the EU purse seine fleet operating in the Indian Ocean

2018

Colloque

Katara I., Gaertner Daniel, Marsac Francis, Grande M., Kaplan David, Urtizberea A., Guery Loreleï, Depetris Mathieu, Duparc Antoine, Floch Laurent, Lopez J., Abascal F.

20ème groupe de travail sur les thons tropicaux

Victoria : CTOI, 2018, 23 p. multigr. (Documents de Réunion-CTOI ; IOTC-2018-WPTT20-36_Rev1).

WPTT.Working Party on Tropical Tunas, 20., Mahé (SYC), 2018/10/29-2018/11/03

The EU purse seine fleet catches of yellowfin tuna (Thunnus albacares) from the Indian Ocean were standardized using the framework described in Katara et al. (2016, 2017) with a Delta-lognormal generalised linear mixed model developed specifically for the standardisation of tropical tuna catch per unit effort (CPUE) time series. With the aim to depict the trend in abundance for adults and for juveniles yellowfin separately, the CPUE time series were treated by fishing mode: free school (FSC) sets and sets associated with floating objects (FOBs). CPUE for FSC was defined as the catch per hour of large yellowfin tuna (> 10 kg). For FOBs sets (i.e., dFAD and log sets), CPUE was defined as the catch per positive set of small yellowfin tuna (<10 kg) - a positive set defined as a set with small yellowfin catches > 0 . Due to the availability of covariates that likely affect them, the time series considered were 1986-2017 and 2010-2017 for FSC and FOB, respectively. . In both cases, the least absolute shrinkage and selection operator method (LASSO) was applied for model selection. A step forward compared to previous years was the inclusion of environmental variables known to affect catchability. Another improvement is the availability of information on dFAD densities, i.e. densities of FOBs with transmitting buoys. This standardization of yellowfin tunas CPUE for the European purse seiners in the Indian Ocean, therefore, represents a significant advance over previous efforts, having used the most recently available data on nontraditional explanatory factors, particularly on dFAD density. Nevertheless, several avenues for future progress are noted in the discussion, such as further improvements in dFAD density estimates and inclusion of additional or different explanatory variables to best represent the impacts of fishery change on CPUE.

Dynamique de populations / modélisation [036MILMAR01] ; Stocks [040PECHE03]

Fonds IRD [F B010085797]

fdi:010085797