Abstract
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.
Highlights
State-space models are a popular tool for fish stock assessments (Gudmundsson, 1994; Aanes et al, 2007; Nielsen and Berg, 2014; Cadigan, 2016; Miller et al, 2016; Aeberhard et al, 2018)
Aldrin et al (2019) presented the following comments about SAM: 1. The data model is in our opinion mis-specified, because the so called “process error” is ignored when the catch data are linked to the true, unknown catch in the population model by the catch equation
We first present the data for analysis, followed by definition of a general stock assessment model consisting of two sub models; a population model for the fish stock and a data model that links observed data to the population model
Summary
State-space models are a popular tool for fish stock assessments (Gudmundsson, 1994; Aanes et al, 2007; Nielsen and Berg, 2014; Cadigan, 2016; Miller et al, 2016; Aeberhard et al, 2018). Most of the many variants consist of a population model for the fish stock and a data model that links observations, such as catch data and survey indices, to unobserved quantities in the population model One such is SAM (state-space assessment model, Nielsen and Berg, 2014; Berg and Nielsen, 2016). We suggest modifications, which will potentially (i) improve fit, (ii) reduce bias and improve prediction performance for catch data and survey indices, and (iii) increase estimated stock size, compared to the present data model in SAM, if the population model is the same. We believe that assessment models cannot be evaluated solely by their ability to fit or predict observed data well Their main purpose is to estimate some unobservable quantities, including present and previous stock sizes. Ma*,y “Preliminary” estimate of Ca,y Observed survey index, “preliminary” estimate of Ia,y(d) Estimate or best guess of Ma,y validation study, predicting the catch data and survey indices (Section 3.2)
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