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 si...
| 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://orbit.dtu.dk/en/publications/8784219e-0481-4cc3-8962-75cc06387100 https://doi.org/10.1139/cjfas-2022-0250 https://backend.orbit.dtu.dk/ws/files/334925538/cjfas-2022-0250.pdf |
| Summary: | 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. |
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