Using a modeling framework for toothfish population dynamics, fishing and data collection, this study investigated how the bias and precision of a CASAL assessment is influenced by various aspects of a tagging program, in particular the effects of the numbers of fish tagged, the duration of a tagging program, the size of tagged fish, and the type of auxiliary data used in the assessment.
The simulation study indicated that:
(1) A high tag size-overlap (at least 60%, better 100%) and high tagging numbers were important particularly in the early stages of an exploratory fishery to ensure some recaptures and maximise the likelihood of a robust assessment.
(2) Using catch-at-age data instead of catch-at-length data substantially improved all assessment estimates and was required for adequately estimate annual recruitment and year class strengths.
(3) For a simulated lightly fished stock, CASAL assessments with good quality tagging data had a low bias and reasonable precision for SSB0 and annual SSB estimates, however the precision of SSB status estimates in the final assessment year was relatively low. Bias and precision of CASAL assessments were better for the lightly fished stock than for a heavily fished stock.
(4) This modeling framework appears to be useful for evaluating data sampling strategies, assessment approaches and management strategies.