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Spatial models enable understanding potential redistribution of marine resources associated with ecosystem drivers and climate change. Stock assessment platforms can incorporate spatial processes, but have not been widely implemented or simulation tested. To address this research gap, an international simulation experiment was organized. The study design was blinded to replicate uncertainty similar to a real‐world stock assessment process, and a data‐conditioned, high‐resolution operating model (OM) was used to emulate the spatial dynamics and data for Indian Ocean yellowfin tuna (Thunnus albacares). Six analyst groups developed both single‐region and spatial stock assessment models using an assessment platform of their choice, and then applied each model to the simulated data. Results indicated that across all spatial structures and platforms, assessments were able to adequately recreate the population trends from the OM. Additionally, spatial models were able to estimate regional population trends that generally reflected the true dynamics from the OM, particularly for the regions with higher biomass and fishing pressure. However, a consistent population biomass scaling pattern emerged, where spatial models estimated higher population scale than single‐region models within a given assessment platform. Balancing parsimony and complexity trade‐offs were difficult, but adequate complexity in spatial parametrizations (e.g., allowing time‐ and age‐variation in movement and appropriate tag mixing periods) was critical to model performance. We recommend expanded use of high‐resolution OMs and blinded studies, given their ability to portray realistic performance of assessment models. Moreover, increased support for international simulation experiments is warranted to facilitate dissemination of methodology across organizations. [ABSTRACT FROM AUTHOR]

Highlights • Vessel effects should be accounted for in the catch rate standardization procedure. • Area-weighting was more important than vessel effects in the standardization. • The nominal index overestimated the abundance trend than did the standardized index. • The spatiotemporal modeling of catch rate data improved the stock assessment. Abstract Spatiotemporal models of catch rate data are used in fisheries science to understand the spatiotemporal dynamics of a fishery and, more importantly, to produce a standardized index of relative abundance for stock assessment models. Here, we apply a spatiotemporal delta-generalized linear mixed model to catch rate data collected from the dolphin-associated purse-seine fishery for yellowfin tuna (Thunnus albacares) in the eastern Pacific Ocean during 1975–2016. Results show that the density of yellowfin tuna had both pronounced spatial variation across the eastern Pacific Ocean and spatiotemporal variation at time scales from quarterly to decadal. In comparison to the index of relative abundance standardized via the spatiotemporal approach, that computed via the nominal approach, which is currently used in the stock assessment, under- and over-estimates initial and terminal abundances, respectively, by as much as 50% or more. The standardized index represents an improvement over the nominal index because the spatiotemporal approach imputes population density in unfished areas to obtain an area-weighted index and accounts for vessel-specific differences in fishing efficiency. The importance of area-weighting for index standardization is highlighted by the fact that ignoring it has a greater effect on the abundance index than does ignoring differences in fishing efficiency among vessels. The stock assessment model that includes standardized indices has significantly larger log-likelihood and reduced pattern in residuals than the one that includes nominal indices, suggesting that replacing nominal indices with indices from a spatiotemporal model can improve the stock assessment for yellowfin tuna in the eastern Pacific Ocean. [ABSTRACT FROM AUTHOR]

We describe and illustrate a spatio-temporal modelling approach for analyzing age- or size-specific catch-per-unit-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°x1°) 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. [ABSTRACT FROM AUTHOR]