An evaluation of estimability of parameters in the state-space non-linear logistic production model

Hyun, Saang-Yoon; Kim, Kyuhan

doi:10.1016/j.fishres.2021.106135

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…As confounding between process variance and observation error parameters is a common and well-documented problem (Auger-Méthé et al, 2016;Dennis et al, 2006;de Valpine and Hilborn, 2005;Hyun and Kim, 2022), and to keep the focus on the effects of systematically biased observations, observation variances were treated as known values. Thus, the parameter vector is Here, we treat N n,0 as a fixed effect parameter (treating it as a random effect merely shifts confounding problems to the hyperparameters if they are estimated and risks assuming away sources of estimability problems that emerge in practice if the hyperparameters are a priori set).…”

Section: Data S Imul Ation and Model Fit Tin Gmentioning

confidence: 99%

“…As confounding between process variance and observation error parameters is a common and well‐documented problem (Auger‐Méthé et al, 2016; Dennis et al, 2006; de Valpine and Hilborn, 2005; Hyun and Kim, 2022), and to keep the focus on the effects of systematically biased observations, observation variances were treated as known values. Thus, the parameter vector is

$$ \boldsymbol{\theta} =\left\{{N}_{n,0},{\boldsymbol{\beta}}_R^T,{\sigma}_{p,R},{\boldsymbol{\beta}}_{S_1}^T,{\sigma}_{p,{S}_1},\dots, {\boldsymbol{\beta}}_{S_{n-1}}^T,{\sigma}_{p,{S}_{n-1}},{\psi}_1,\dots, {\psi}_n\right\}.

…”

Section: Data Simulation and Model Fittingmentioning

confidence: 99%

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Combining multiple data sources with different biases in state‐space models for population dynamics

Polansky

Mitchell

Newman

2023

Ecology and Evolution

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Section: Data S Imul Ation and Model Fit Tin Gmentioning

confidence: 99%

$$ \boldsymbol{\theta} =\left\{{N}_{n,0},{\boldsymbol{\beta}}_R^T,{\sigma}_{p,R},{\boldsymbol{\beta}}_{S_1}^T,{\sigma}_{p,{S}_1},\dots, {\boldsymbol{\beta}}_{S_{n-1}}^T,{\sigma}_{p,{S}_{n-1}},{\psi}_1,\dots, {\psi}_n\right\}.

…”

Section: Data Simulation and Model Fittingmentioning

confidence: 99%

Combining multiple data sources with different biases in state‐space models for population dynamics

Polansky

Mitchell

Newman

2023

Ecology and Evolution

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“…Such estimation problems seem to arise when SSMs fail to separate out the two different types of errors (i.e., observation and process errors) from each other. Previous studies discovered that those estimation issues tend to occur when observation error is larger than process error (Dennis et al, 2006;Auger-Méthé et al, 2016;Hyun and Kim, 2022), but our preliminary study indicated that the existence of trends in time series data can also affect the estimability of model parameters in SSMs.…”

Section: Introductionmentioning

confidence: 63%

“…Although a SSPM has a relatively simple structure compared to other contemporary stock assessment models, the likelihood surface may be almost flat or have long level ridges (Hyun and Kim, 2022), so that estimability of its model parameters is often questionable, thus requiring some external aids (e.g., constraints on parameters) for the successful convergence of a model (Millar and Meyer, 2000;McAllister et al, 2001;Punt, 2003;Ono et al, 2012;Parent and Rivot, 2012;Winker et al, 2018Winker et al, , 2020. We consider parameters to be estimable if a unique set of estimates that optimise the likelihood function exists (Auger-Méthé et al, 2016;Auger-Méthé et al, 2021).…”

mentioning

confidence: 99%

“…We consider parameters to be estimable if a unique set of estimates that optimise the likelihood function exists (Auger-Méthé et al, 2016;Auger-Méthé et al, 2021). This condition is almost never satisfied in a SSPM, unless one incorporates extra constraints on the model parameters (Hyun and Kim, 2022). Those constraints are applied through a set of assumptions, or by incorporating strong prior information on model parameters.…”

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confidence: 99%

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State-space stock assessment models for data-moderate ﬁsheries

Kim¹

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Fish populations are subject to natural growth, environmental pressures, and natural mortality. In addition, they may experience pressure from anthropic fishing mortality. Management of fish stocks requires the collection of suitable data from which population models can be built. State-space models (SSMs) are one modelling approach, and this project investigates their application to data-moderate stocks. We define data-moderate stocks as those for which there are no survey data, no information on age composition, and fisheries-dependent data are the only available source of information. We find that many existing state-space models are either too simple (e.g., state-space surplus production models) or too complex (e.g., state-space age-structured models) for these stocks, although many fisheries around the world face data-moderate situations. A state-space model is becoming a favoured choice in modelling fish population dynamics, as it allows one to incorporate both measurement and process errors. However, several studies have found that separation of the two sources of variability can result in estimability problems even in simple state-space models. Using a state-space surplus production model as an example, we found that such estimability problems can occur even in a simple stock assessment model, especially when measurement error is large relative to process error. This problem even exists when constraints are imposed on most of the model parameters. Such findings suggest the limitations of SSMs and the importance of model diagnostics. Using data collected from South Korean fish stocks as application examples, we developed two stock assessment models in state-space form. The first model is a state-space two-life stage-structured production model which can be applied to stocks where juvenile and adult fish have been separately exploited by different fisheries. The key feature of the model is that in the absence of any composition data (e.g., age and size), demographic relationships between juvenile and adult populations are incorporated using abundance indices collected from different fisheries, each of which selectively targets the two different life stages of fish. The second model is a state-space length-based age-structured model. This integrated model is developed to utilise length composition data to inform the age structure of a population. Such data are often available in many data-moderate stocks, instead of a direct measure of age composition, such as catch-at-age data. Separating age groups based on length compositions is not a new concept, but most existing models do not allow process error. Thus, the development of such a model in state-space form could provide a more reliable assessment tool for many data-moderate stocks. This thesis research contributes to the better understanding of potential estimability issues in SSMs for fish stock assessments, as well as development of the two new state-space models for data-moderate fisheries. We also identified several issues associated with our findings which could be useful for future research.

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Application of a Length-Based Stock Assessment Model for the Chub Mackerel (Scomber japonicus) in Korean Waters

Hyun

2022

Ocean Sci. J.

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An evaluation of estimability of parameters in the state-space non-linear logistic production model

Cited by 6 publications

References 24 publications

Combining multiple data sources with different biases in state‐space models for population dynamics

Combining multiple data sources with different biases in state‐space models for population dynamics

State-space stock assessment models for data-moderate ﬁsheries

Application of a Length-Based Stock Assessment Model for the Chub Mackerel (Scomber japonicus) in Korean Waters

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