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Most integrated stock assessment models are fit to alternative sources of data like indices of abundance and length/age composition of catches in specific fisheries. While indices of abundance are often standardized over time, not much attention is paid to the temporal stability of the length/age data. A sequential approach to fitting model outputs to all sources of data, varying the weight given to the length composition data, for Indian Ocean bigeye tuna (Thunnus obesus) is examined in this paper. The sensitivity of the bigeye tuna stock assessment to assumptions regarding the size-selectivity of key fisheries and the relative weight of size frequency data in the stock assessment is examined. Logistic, double normal, and cubic spline selectivity functions are used to model the size composition of catches in the main industrial fisheries (longline and purse seine). Overall, there is a poor fit of stock assessment models to the individual length frequency observations collected from these fisheries, although marginal improvements of fit were made when temporally variable selectivity was implemented in the SS-III framework using the above described functions. The most influential factor in the assessment was the weighting of the length composition data relative to the indices of stock abundance. Contradictory signals between these two data sources have a large effect on spawning biomass dynamics, and inference based on these weightings can produce different management conclusions. We examined alternative hypotheses and discuss the merits of fitting to all sources of data, or discounting some information if it has been unreliably collected over time. We emphasize that understanding the data is key to performing a well-calibrated stock assessment, and further refinements to the approach pursued here are discussed.
