|A-scala: un análisis estadístico de captura a talla estructurado por edad para la evaluación de las poblaciones de atunes en el Océano Pacifico oriental = A-scala: an age-structured statistical catch-at-length analysis for assessing tuna stocks in the eastern Pacific Ocean|
Maunder, M.N.; Watters, G.M. (2003). A-scala: un análisis estadístico de captura a talla estructurado por edad para la evaluación de las poblaciones de atunes en el Océano Pacifico oriental = A-scala: an age-structured statistical catch-at-length analysis for assessing tuna stocks in the eastern Pacific Ocean. Bull. I-ATTC/Bol. CIAT 22(5): 435-582
In: Bulletin. Inter-American Tropical Tuna Commission = Boletín. Comisión Interamericana del Atún Tropical. CIAT/I-ATTC: La Jolla, Calif. ISSN 0074-0993, more
Stock assessment; Thunnus albacares (Bonnaterre, 1788) [WoRMS]; Thunnus obesus (Lowe, 1839) [WoRMS]; ISE, East Pacific Rise [Marine Regions]; Marine
|Authors|| || Top |
- Maunder, M.N.
- Watters, G.M.
We describe an age-structured statistical catch-at-length analysis (A-SCALA) based on the MULTIFAN-CL model of Fournier et al. (1998). The analysis is applied independently to both the yellow fin and the bigeye tuna populations of the eastern Pacific Ocean (EPO). We model the populations from 1975 to 1999, based on quarterly time steps. Only a single stock for each species is assumed for each analysis, but multiple fisheries that are spatially separate are modeled to allow for spatial differences in catch ability and selectivity. The analysis allows for error in the effort-fishing mortality relationship, temporal trends in catch ability, temporal variation in recruitment, relationships between the environment and recruitment and between the environment and catch ability, and differences in selectivity and catch ability among fisheries. The model is fit to total catch data and proportional catch-at-length data conditioned on effort. The A-SCALA method is a statistical approach, and therefore recognizes that the data collected from the fishery do not perfectly represent the population. Also, there is uncertainty in our knowledge about the dynamics of the system and uncertainty about how the observed data relate to the real population. The use of likelihood functions allow us to model the uncertainty in the data collected from the population, and the inclusion of estimable process error allows us to model the uncertainties in the dynamics of the system. The statistical approach allows for the calculation of confidence intervals and the testing of hypotheses. We use a Bayesian version of the maximum likelihood framework that includes distributional constraints on temporal variation in recruitment, the effort-fishing mortality relationship, and catch ability. Curvature penalties for selectivity parameters and penalties on extreme fishing mortality rates are also included in the objective function. The mode of the joint posterior distribution is used as an estimate of the model parameters. Confidence intervals are calculated using the normal approximation method. It should be noted that the estimation method includes constraints and priors and therefore the confidence intervals are different from traditionally calculated confidence intervals. Management reference points are calculated, and forward projections are carried out to provide advice for making management decisions for the yellow fin and bigeye populations.