Implications of model and data assumptions: An illustration including data for the Taiwanese longline fishery into the eastern Pacific Ocean bigeye tuna ( Thunnus obesus) stock assessment

Abstract

Assumptions about model structure and data are a critical part of the fisheries stock assessment procedure. Sensitivity to these assumptions can be greater than the estimation uncertainty conditioned on the assumptions. Therefore, it is important that model structure and data assumptions be evaluated in stock assessments. We use the stock assessment of bigeye tuna in the eastern Pacific Ocean to illustrate several issues related to typical model and data assumptions. For this purpose, we treat the Taiwanese longline fishery as a separate entity, rather than combining data for that fishery with those for other longline fisheries, as conducted in the latest assessment of bigeye. Using the bigeye case study, we illustrate that incorrect assumptions about the relationship between catch-per-unit-effort (CPUE) and abundance and the form of the selectivity curve can greatly impact the assessment results, even when the fishery associated with the data is only a minor component of the total catch. We also illustrate the importance that understanding the data and the system under investigation plays in avoiding making inappropriate assumptions.