DOI: 10.5061/dryad.v6wwpzh3s
title: Indian Ocean Tuna Commission yellowfin tuna stock synthesis assessment grid

description: 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
description: 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.
description: # Indian Ocean Tuna Commission yellowfin tuna stock synthesis assessment
 grid
 [https://doi.org/10.5061/dryad.v6wwpzh3s](https://doi.org/10.5061/dryad.v6wwpzh3s) ## Description of the data and file structure The analysis was based on running 1440 different stock assessment scenarios. This requires SS3 input and control files for each scenario. The outputs from SS were then processed into summary R data.frames. There are therefore 4 sets of files i) SS3 inputs and control files, ii) summary R data.frames, iii) R scripts for running the analyses, i.e., historical assessments and hindcasts, including as array job on a cluster, and iv) post-processing, i.e., combining outputs into common files. ## i) Stock synthesis inputs The stock synthesis control and data files are provided along with the outputs ## ii) Summary data sets ### Time series ts.RData ### Summary of reference points and likelihood components smry.RData ### Prediction skill, i.e., MASE mase.RData ### Retrospective analysis, i.e., Mohn's rho mohn.RData ### Current status i.e., SSB/BMSY and F/FMSY for kobe phase plot kb.RData ### Predicted surplus production and catch for SSB from 0 to Virgin sp.RData ## Code ### iii) Running The code is in the form of R scripts called by a qsub script on a cluster to run several independent but similar jobs in parallel. A single array job is an efficient way to run a large set of parameterised tasks. #### Array jobs on cluster ##### Runs historical assessments runArray.qsub, runArray.R ##### Runs hindcast as an array job on cluster runAlbioHind.qsub, runAlbioHind.R #### Parallel stock synthesis grid runSS.R #### Parallel hindcast across the grid ssDiagsHindcast.R ### iv) Summary #### Read outputs from each assessment and combine process.R #### Create figures for Manuscript albio-figs-Plos.Rmd #### Various ad-hoc analysis albio-figs-V1.Rmd