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Extending production models to include process error in the population dynamics



Extending production models to include process error in the population dynamics



Canadian Journal of Fisheries and Aquatic Sciences ober; 60(10): 1217-1228



Four methods for fitting production models, including three that account for the effects of error in the population dynamics equation (process error) and when indexing the population (observation error), are evaluated by means of Monte Carlo simulation. An estimator that represents the distributions of biomass explicitly and integrates over the unknown process errors numerically (the NISS estimator) performs best of the four estimators considered, never being the worst estimator and often being the best in terms of the medians of the absolute values of the relative errors. The total-error approach outperforms the observation-error estimator conventionally used to fit dynamic production models, and the performance of a Kalman filter based estimator is particularly poor. Although the NISS estimator is the best-performing estimator considered, its estimates of quantities of management interest are severely biased and highly imprecise for some of the scenarios considered.

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Accession: 009846477

Download citation: RISBibTeXText

DOI: 10.1139/f03-105



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