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Extending production models to include process error in the population dynamics

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Abstract:

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.

Une simulation de Monte Carlo permet d'évaluer quatre méthodes d'ajustement des modèles de production, dont trois qui prennent en compte les effets des erreurs dans la dynamique de population (erreurs de processus) et dans l'indexation de la population (erreurs d'observation). Des quatre estimateurs examinés, l'estimateur NISS, qui représente la distribution de la biomasse de façon explicite et qui fait numériquement l'intégration des erreurs de processus inconnues, fonctionne le mieux; il n'est jamais le pire et est très souvent le meilleur en ce qui concerne les médianes des valeurs absolues des erreurs relatives. La méthode de l'erreur totale fonctionne mieux que celle de l'erreur d'observation couramment utilisée dans l'ajustement de modèles dynamiques de production; la méthode qui utilise un filtre de Kalman fonctionne particulièrement mal. Bien que la méthode NISS soit la meilleure des méthodes étudiées, les estimations d'intérêt pour la gestion qu'elle produit sont particulièrement faussées et très imprécises dans certains scénarios.[Traduit par la Rédaction]

Document Type: Research Article

Publication date: October 1, 2003

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  • Published continuously since 1901 (under various titles), this monthly journal is the primary publishing vehicle for the multidisciplinary field of aquatic sciences. It publishes perspectives (syntheses, critiques, and re-evaluations), discussions (comments and replies), articles, and rapid communications, relating to current research on cells, organisms, populations, ecosystems, or processes that affect aquatic systems. The journal seeks to amplify, modify, question, or redirect accumulated knowledge in the field of fisheries and aquatic science. Occasional supplements are dedicated to single topics or to proceedings of international symposia.
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