期刊
FISHERIES RESEARCH
卷 229, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.fishres.2020.105585
关键词
Stock assessment; State-space model; Population model; Data model; Hierarchical model; Cross-validation
类别
资金
- Research Council of Norway [3680_14809]
- Institute of Marine Research, Norway [3680_14809]
- Rammeavtale for stotte til statistisk metodeutvikling og analyse - Saksnr [2016/1011]
This paper considers a general state-space stock assessment modeling framework that integrates a population model for a fish stock and a data model. This way observed data are linked to unobserved quantities in the population model. Using this framework, we suggest two modifications to improve accuracy in results obtained from the stock assessment model SAM and similar models. The first suggestion is to interpret the process error in these models as stochastic variation in natural mortality, and therefore include it in the data model. The second suggestion is to consider the observed catch as unbiased estimates of the true catch and modify the observation error accordingly. We demonstrate the efficacy of these modifications using empirical data from 14 fish stocks. Our results indicate that the modifications lead to improved fits to data and prediction performance, as well as reduced prediction bias.
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