Using self-testing to validate the SURBAR survey-based assessment model

Abstract

SURBAR is a survey-based fisheries stock assessment model that is used to indicate relative stock dynamics and provide advice for both data-rich and data-poor fisheries. It is a relatively simple separable model that requires only a survey index-at-age to determine stock trends (although data on weights and maturity can also be included). This paper evaluates the characteristics and potential utility of SURBAR by applying it to data generated by the model itself, considering the impact of three parameters (survey noise, smoothing, and the time of year of the survey) on the ability of the model correctly to reconstruct self-generated populations. We show that, while SURBAR is generally able to achieve this reconstruction, errors can be induced by either of the three tested parameters that may not be detectable by standard goodness-of-fit measures, and which could lead to inappropriate management advice. The analysis emphasises the importance of understanding the likely characteristics and behaviour of the model before using it to provide advice on fisheries stock dynamics.