Using data-limited approaches to assess data-rich Indian Ocean bigeye tuna: Data quantity evaluation and critical information for management implications

The majority of fishery stocks in the world are data limited, which limits formal stock assessments. Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing precautionary management strategies. We compare catch advice obtained from applications of various data-limited methods (DLMs) with forecasted catch advice from existing data-rich stock assessment models for the Indian Ocean bigeye tuna (Thunnus obesus). Our goal was to evaluate the consistency of catch advice derived from data-rich methods and data-limited approaches when only a subset of data is available. The Stock Synthesis (SS) results were treated as benchmarks for comparison because they reflect the most comprehensive and best possible scientific information of the stock. This study indicated that although the DLMs examined appeared robust for the Indian Ocean bigeye tuna, the implied catch advice differed between data-limited approaches and the current assessment, due to different data inputs and model assumptions. Most DLMs tended to provide more optimistic catch advice compared with the SS, which was mostly influenced by historical catches, current abundance and depletion estimates, and natural mortality, but was less sensitive to life-history parameters (particularly those related to growth). This study highlights the utility of DLMs and their implications on catch advice for the management of tuna stocks.