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Detecting significant retrospective patterns in state space fish stock assessment

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Retrospective patterns are commonly investigated to validate fish stock assessment models. A widely applied measure for retrospective bias is Mohn's ρ and corresponding retrospective plots. However, retrospective patterns can be… Click to show full abstract

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

Keywords: assessment; state space; fish stock; stock assessment; significant retrospective; retrospective patterns

Journal Title: Canadian Journal of Fisheries and Aquatic Sciences
Year Published: 2023

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