Discriminating alternative stock–recruitment models and evaluating uncertainty in model structure

Zhou, Shijie

doi:10.1016/j.fishres.2007.06.026

Cited by 28 publications

(17 citation statements)

References 64 publications

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“…Normal random errors were added to the predicted length increments produced by each operating model in each simulated data set, and the variance was equal for each operating model. This gave all 3 operating models the same process and observation errors (Zhou 2007) and reflects the typical distributions observed in the size Figure 1. (A-C) The von Bertalanffy, Gompertz, and inverse logistic growth models fitted to tag-recapture data that were used as the training data set. The data are from Black Island (42.96878 S,145.49248 E) and were the best example of tag-recapture data in terms of sample size and initial size range (A).…”

Section: Simulating Testing Data Setssupporting

confidence: 63%

“…The SD of the residuals was estimated and stored along with model parameter values and related model fits at each run. In each case, the candidate model that was related mathematically to the operating model was termed the correct model, whereas models that were unrelated to the operating model were termed the incorrect models (Zhou 2007). …”

Section: Criterion 1: Model Selection and Error Ratementioning

confidence: 99%

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Growth Models for Fisheries: The Effect of Unbalanced Sampling Error On Model Selection, Parameter Estimation, and Biological Predictions

Helidoniotis

Haddon

2013

Journal of Shellfish Research

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Field studies that attempt to estimate the mean growth rates of individuals in a population usually yield unbalanced data and various forms of sampling error. If these data are a poor representation of a population, then the estimates of growth rates may also be unrepresentative of the population. We present an assessment of the performance and uncertainty in 4 growth models-the von Bertalanffy, Gompertz, inverse logistic, and Schnute models-fitted to data with various scenarios of sampling error. The performance of each model was determined by comparing highest likelihoods outcomes to known case outcomes. A Monte Carlo simulation framework was used to generate data consisting of 8 typical scenarios of sampling error common in tag-recapture data. Each growth model was evaluated according to 2 metrics: the error rate (i.e., a metric for model uncertainty) and the prediction error (i.e., the accuracy of biological predictions such as age). Results indicate that an inadequate size range in the data (i.e., a lack of juvenile size classes) would often lead to a high error rate. When negative growth increment data are included, the K parameter of the von Bertalanffy model increased, and the L N decreased. The inverse logistic model sometimes produced absurd parameter estimates but nevertheless generated the lowest prediction errors. A high prediction error can potentially have far more serious implications to fishery stock assessments than is currently appreciated. Given widespread use of the von Bertalanffy and Gompertz models, selected solely on the basis of model selection criteria, it is clear that greater care and scrutiny are warranted in the selection of growth models in the presence of sampling error.

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Section: Simulating Testing Data Setssupporting

confidence: 63%

Section: Criterion 1: Model Selection and Error Ratementioning

confidence: 99%

Growth Models for Fisheries: The Effect of Unbalanced Sampling Error On Model Selection, Parameter Estimation, and Biological Predictions

Helidoniotis

Haddon

2013

Journal of Shellfish Research

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“…In the literature, choosing an appropriate function form has been identified as perhaps the most challenging in the solution of Problem 1 [38]. This is partly because there are no robust statistical techniques for exploring the fundamental biological processes in the SRF.…”

Section: Problem 1 (The General Srf Problem)mentioning

confidence: 99%

Regularization of a Parameter Estimation Problem using Monotonicity and Convexity Constraints

Subbey¹

2017

ESAIM: ProcS

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Abstract. In marine science, it is usually assumed that there is a functional relationship between the parental population size and subsequent offsprings. The function is referred to as the Stock Recruitment Function (SRF). Determining the SRF translates to the optimization problem of estimating a set of parameters using past and sparse observation, which are usually of modest accuracy. The problem is challenging because several candidate functions exist in the literature, and the choice of best function is non-trivial, due to data sparsity and uncertainty.This paper formulates the problem as a constrained optimization task, and uses B-spline basis functions to represent the functional family to which the SRF belongs. Regularized solutions are obtained by requiring that the derived functions are both monotone and convex.The approach presents two major contributions to the existing computational challenges:• It avoids the non-trivial problem of choosing the functional form a priori.• Regularization of the problem using constraints ensures that parameter estimates are realistic. Numerical examples are presented to compare 1 and 2-norm solutions.Résumé. The paper presents a method that uses a prori information to regularize an inverse problem in fisheries science. It demonstrates the efficacy of the methodology in dealing with sparse and uncertain data, and limiting assumptions on the solution space.

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“…Zhou (2007) used artificially generated datasets with differing underlying stockrecruitment models to indicate that the correct model may not be distinguishable based on statistical methods or information criteria. Caution against using statistical inferences to evaluate spawnerrecruitment relations is advised (de Valpine and Hastings, 2002;Maceina and Pereira, 2007).…”

Section: Stock-recruitment Curvesmentioning

confidence: 99%

“…Caution against using statistical inferences to evaluate spawnerrecruitment relations is advised (de Valpine and Hastings, 2002;Maceina and Pereira, 2007). Rather, knowledge of biology and behavior of specific species may provide insight into which model is appropriate (Zhou, 2007). It has been argued that that the Beverton-Holt relation is more appropriate to describe coho behavior than the Ricker curve (Barrowman and others, 2003).…”

Section: Stock-recruitment Curvesmentioning

confidence: 99%

Skagit River coho salmon life history model—Users’ guide

Woodward¹,

Kirby²,

Morris³

2017

Open-File Report

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Discriminating alternative stock–recruitment models and evaluating uncertainty in model structure

Cited by 28 publications

References 64 publications

Growth Models for Fisheries: The Effect of Unbalanced Sampling Error On Model Selection, Parameter Estimation, and Biological Predictions

Growth Models for Fisheries: The Effect of Unbalanced Sampling Error On Model Selection, Parameter Estimation, and Biological Predictions

Regularization of a Parameter Estimation Problem using Monotonicity and Convexity Constraints

Skagit River coho salmon life history model—Users’ guide

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