Project C.1.a - Purse-seine catch composition bias estimation
Program(s) in charge: Stock Assessment Program
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- Explore and develop robust statistical models to investigate and correct the possible bias in tuna catch composition, resulting from data loss during the COVID-19 pandemic of 2020-2021.
- The COVID-19 pandemic hindered collection of port-sampling data in 2020-2021.
- Some of the ports most affected were where bigeye tuna (BET) catch is unloaded.
- Port-sampling data are used to estimate the tropical tuna catch composition of the purse-seine fleet, and thus, there is concern that the Best Scientific Estimates of catch may be biased, particularly for bigeye tuna.
- Spatio-temporal (CAR) models to estimate port-sampling species proportions from observer (logbook) data with overall good performance were developed for 2020-2021 (SAC-13-05).
- Simulation results suggest the CAR model performance is robust to the type of systematic data loss that occurred in 2020. However, simulation studies need to be conducted to evaluate the robustness of the CAR model 2021 estimates.
- Because the stock assessment models have a quarterly time step and the fisheries definitions differ from the areas used in the CAR modeling, it will also be important to developmentdevelop fine-scale spatio-temporal models (e.g., 5°- month or 5°- quarter).
- Relevance for management
- Revised catch estimates for the purse-seine fishery will be essential for the benchmark assessments in 2023 and 2024.
- 18 months
- Workplan and status
- 2022: Further investigate spatio-temporal modeling options to correct possible bias in tuna catch composition estimates for all three purse-seine set types.
- 2023: Produce revised catch composition estimates for the purse-seine fishery for 2020-2021.
- Reports for the SAC and the Commission; publications in peer-reviewed journals.