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 ρ and corresponding retrospective plots. However, retrospective patterns can be interpreted differently by experts. To make decisions regarding signifi...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
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Subjects: | |
Online Access: | https://hdl.handle.net/11250/3103618 https://doi.org/10.1139/cjfas-2022-0250 |
Summary: | 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 proposed a post-sample Mohn’s ρ 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 ρ 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 ρ is useful for detecting violations associated with bias, but not for violations associated with variances and correlations. publishedVersion |
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