eCite Digital Repository

Integrated toothfish stock assessments using Casal2

Citation

Dunn, A and Gruss, A and Devine, JA and Miller, C and Ziegler, P and Maschette, D and Earl, T and Darby, C and Massiot-Granier, F, Integrated toothfish stock assessments using Casal2, Commission for the Conservation of Antarctic Marine Living Resources, 12 June (2022) [Refereed Conference Paper]


Preview
PDF
Pending copyright assessment - Request a copy
948Kb
  

Official URL: https://meetings.ccamlr.org/en/wg-sam-2022/14

Abstract

Abstract

The software platform CASAL (C++ algorithmic stock assessment laboratory) has been used to implement integrated statistical catch-at-age toothfish stock assessments in CCAMLR since 2005. However, CASAL is no longer being developed or maintained. The software platform Casal2 has been developed as a more generalised framework for implementing Bayesian integrated catch-at-age (or catch-at-length) stock assessments to replace CASAL. Its greater computational power allows Casal2 more flexibility in specifying population dynamics, parameter estimation and model outputs than CASAL.

In this report, we present a comparison of CASAL and Casal2 model implementations using the 2021 CASAL assessments of Antarctic toothfish (Dissostichus mawsoni) in Subareas 88.1/88.2A-B (Ross Sea region) and Patagonian toothfish (Dissostichus eleginoides) in Subarea 48.3 (South Georgia).  Model comparisons for the assessments in Divisions 58.5.1 (Kerguelen Islands) and 58.5.2 (Heard Island and McDonald Islands) are still ongoing. The results from the CASAL models are compared to the results from Casal2 models, including estimates of initial and current status, CCAMLR precautionary yields, and model output diagnostics.

The comparisons show that the two software packages provide equivalent estimates of key parameters for the case studies used. For the case studies, the diagnostics derived from the CASAL and Casal2 models provided identical conclusions on model fits. The estimation process in Casal2 was significantly faster, allows for models to be fitted with a larger memory footprint than CASAL, and Markov chain Monte Carlo (MCMC) estimates from Casal2 were typically obtained in about 10 to 50% of the time required to run the equivalent CASAL model. CASAL and Casal2 also performed the same when observations were simulated with CASAL (as an operating model) and the simulated data fitted with CASAL or Casal2.

We show that Casal2 has been validated for use for integrated toothfish stock assessments at CCAMLR, and we have successfully implemented Casal2 models for Antarctic toothfish in Subareas 88.1/88.2A-B (Ross Sea region) and Patagonian toothfish in Subarea 48.3 (South Georgia). Advice on the status of the stocks and their associated catch levels, consistent with the CCAMLR Decision Rule, would not change with the use of Casal2.    

We recommend that:

  1. Casal2 be accepted as being validated for use by CCAMLR for integrated statistical catch-at-age toothfish stock assessments.
  2. CASAL models be presented alongside the equivalent Casal2 models for the next toothfish stock assessments to CCAMLR to further demonstrate the equivalence of the CASAL and Casal2 software packages.
  3. The guidelines given in Appendix B of the present report for validating Casal2 files be considered for any Casal2 models presented to CCAMLR.
  4. The version of Casal2 used is described in assessment reports, and models are validated using ‘asserts’ with backwards compatibility checks for each model implemented using Casal2.
  5. Casal2 compatibility switches used for equivalence with CASAL be set to the ‘casal’ option for comparing between CASAL and Casal2, and to the default ‘casal2’ option for new models using Casal2.
  6. Further research be encouraged to consider the use of parameter transformations (log, average-difference, and simplex) to improve stability and MCMC performance in Casal2 models.

Item Details

Item Type:Refereed Conference Paper
Keywords:fisheries, Antarctica, assessment, toothfish
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Fisheries sciences
Research Field:Fisheries management
Objective Division:Animal Production and Animal Primary Products
Objective Group:Fisheries - wild caught
Objective Field:Fisheries - wild caught not elsewhere classified
UTAS Author:Maschette, D (Mr Dale Maschette)
ID Code:155014
Year Published:2022
Deposited By:Sustainable Marine Research Collaboration
Deposited On:2023-01-23
Last Modified:2023-01-25
Downloads:0

Repository Staff Only: item control page