A Bayesian approach to identifying mixtures from otolith chemistry data

Authors: Munch, S. B.; Clarke, L. M.

Source: Canadian Journal of Fisheries and Aquatic Sciences, Volume 65, Number 12, December 2008 , pp. 2742-2751(10)

Publisher: NRC Research Press

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Abstract:

Studies investigating population structure and mixed-stock composition of fish populations frequently use otolith chemistry as a natural tool for discerning stocks. Current methods for estimating mixed-stock composition, however, assume complete accuracy in the training data, which is often not the case. Here we present a method for estimating mixed-stock composition using multivariate continuous data that accounts for uncertainty in the training data. Application of the method to previously reported data for natal homing in weakfish (Cynoscion regalis) and simulations based on these data revealed that for sample sizes greater than about 30, the present method provides results that are quite similar to those of previous methods. An advantage of this Bayesian approach over other methods, however, is the ease with which functionals of the model, such as migration distance and direction, can be calculated. It also provides simple means of visualizing spatial structure in the classification probabilities and migration patterns.

Les recherches qui s’intéressent à la structure de population et à la composition des stocks mixtes chez les populations de poissons utilisent souvent la chimie des otolithes comme outil naturel pour séparer les stocks. Cependant, les méthodes courantes pour estimer la composition des stocks assument que les données d’entraînement sont parfaitement exactes, ce qui n’est souvent pas le cas. Nous présentons ici une méthode pour estimer la composition de stocks mixtes qui utilise des données multidimensionnelles continues qui tiennent compte de l’incertitude dans les données d’entraînement. L’utilisation de la méthode avec des données publiées antérieurement sur le retour au lieu de naissance chez l’acoupa royal (Cynoscion regalis) et des simulations basées sur ces données montrent que, pour des échantillons supérieurs à environ 30, notre méthode fournit des résultats qui sont très semblables à ceux des méthodes antérieures. Un avantage de notre approche bayésienne par rapport aux autres méthodes est la facilité avec laquelle on peut calculer les fonctionnelles du modèle, telles que la distance et la direction de la migration. Elle procure aussi un moyen simple de visualiser la structure spatiale dans les probabilités de la classification et les patrons de migration.

Document Type: Research Article

Publication date: December 1, 2008

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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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