The need for spatio-temporal modeling to determine catch-per-unit effort based indices of abundance and associated composition data for inclusion in stock assessment models

We describe and illustrate a spatio-temporal modelling approach for analyzing age- or size-specific catch-perunit-effort (CPUE) data to develop indices of relative abundance and associated composition data. The approach is based on three concepts: 1) composition data that are used to determine the component of the population represented by the index should be weighted by CPUE (abundance) while the composition data used to represent the fish removed from the stock should be weighted by catch; 2) due to spatial non-randomness in fishing effort and fish distribution, the index, index composition, and catch composition, should be calculated at a fine spatial scale (e.g., 1 degrees x1 degrees) and summed using area weighting; and 3) fine-scale spatial stratification will likely result in under-sampled and unsampled cells and some form of smoothing method needs to be applied to inform these cells. We illustrate the concepts by applying them to yellowfin tuna (Thunnus albacares) in the eastern Pacific Ocean.