The Tuna Regional Fisheries Management Organisations commonly develop uncertainty grids to condition models in integrated stock assessments to account for uncertainties in parameters that cannot be estimated from the data. For example, by constructing multidimensional grids that consist of different plausible combinations of assumptions, fixed parameter values, and data sets. It is not always clear, however, whether this is intended as an uncertainty analysis or a sensitivity analysis. The choice of assessment model scenarios and ways of estimating uncertainty has an impact on the risk of exceeding the limit and missing target reference points. Therefore, to better understand the impact of uncertainty on stock assessment advice and the risk of failing to meet conservation and sustainability objectives, we used the uncertainty grid developed by the Indian Ocean Tuna Commission (IOTC) for albacore tuna (Thunnus alalunga), a full factorial design with 1,440 model configurations (IOTC, 2019). This is sufficient to provide contrast, but not too big to be unmanageable. 

IOTC. 2019 (23-27 July 2019). Report of the 7th Session of the IOTC Working Party on Temperate Tunas: Assessment Meeting. Tech. rept. IOTC-2019-WPTmT07(AS)-R. Indian Ocean Tuna Commission, Shizuoka, Japan

It was compiled by the Indian Ocean Tuna Commission. There are two parts i) a grid of input datasets comprising catch, catch per unit effort, size data, and biological parameters with associated control file; and ii) the fits to the data using stock synthesis.

Using i) a variety of runs can be conducted, i.e., historical fits to the data, retrospective analyses, hindcasts, projections, bootstraps, and MCMC. Under ii) summaries as used in "Empirical Validation and Risk Equivalence: A Pathway to Resilient Fisheries Management" Kell et al., 2024 are provided as R data.frames.