Detecting significant retrospective patterns in state space fish stock assessment

Retrospective patterns are commonly investigated to validate fish stock assessment models. A widely applied measure for retrospective bias is Mohn's p and corresponding retrospective plots. However, retrospective patterns can be interpreted differently by experts. To make decisions regarding significant retrospective patterns less subjective, we proposed a post-sample Mohn's p significance test. As case studies, we applied the state space assessment model SAM with data on Northeast Arctic cod and Norwegian coastal cod north of 67°N. We showed that the acceptance regions of Mohn's p depends on both the data available and the assessment model complexity. We also assessed the test power under a range of assumption violations and conclude that Mohn's p is useful for detecting violations associated with bias, but not for violations associated with variances and correlations.