Investigating the influence of length–frequency data on the stock assessment of Indian Ocean bigeye tuna

Abstract

Most integrated stock assessment models are fitted 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) was examined in this paper. Logistic, double normal, and cubic spline selectivity functions were used to model the size composition of catches in the main industrial fisheries (longline and purse seine). Overall, there was a poor fit of stock assessment models to the individual length frequency observations collected from these fisheries, although marginal improvements of fit was made when temporally variable selectivity was implemented in the Stock Synthesis 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 emphasized that understanding the data was the key to performing a well-calibrated stock assessment, and further refinements to the approach pursued in the analysis presented are discussed.