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Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method

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We present a hierarchical Bayesian modelling (HBM) framework for estimating riverine fish population size from successive removal data via electrofishing. It is applied to the estimation of the population of Atlantic salmon (Salmo salar) juveniles in the Oir River (France). The data set consists of 10 sampling sites sampled by one or two removals over a period of 20 years (1986–2005). We develop and contrast four models to assess the effect of temporal variations and habitat type on the density of fish and the probability of capture. The Bayes factor and the deviance information criterion are used to compare these models. The most credible and parsimonious model is the one that accounts for the effects of the years and the habitat type on the density of fish. It is used to extrapolate the population size in the entire river reach. This paper illustrates that HBM successfully accommodates large but sparse data sets containing poorly informative data for some units. Its conditional structure enables it to borrow strength from data-rich to data-poor units, thus improving the estimations. Predictions of the population size of the entire river reach can be derived, while accounting for all sources of uncertainty.

Nous proposons un cadre de modélisation bayésien hiérarchique (HBM) pour estimer l’abondance d’une population de juvéniles de saumon atlantique (Salmo salar) dans la rivière Oir (France) par la méthode des retraits successifs par pêche électrique. Le jeu de données est composé de 10 sites d’échantillonnage, chacun ayant été échantillonné par un ou deux passages sur une période de 20 ans (1986–2005). Quatre modèles sont développés pour introduire les variations inter-annuelles et les effets du type d’habitat sur la densité et sur la probabilité de capture. Ces modèles sont comparés à l’aide du facteur de Bayes et d’un critère d’information basé sur la déviance. Le modèle retenu est celui qui prend en compte l’effet de l’année et du type d’habitat sur la densité de juvéniles de saumons. Il est utilisé pour extrapoler la population de saumon à l’ensemble du cours d’eau. Cet article illustre que les HBM permettent de traiter des jeux de données de grande taille dont l’information portée par chaque unité échantillonnée est hétérogène. La structure conditionnelle permet d’améliorer les estimations car elle organise un transfert d’information entre les unités. Le modèle permet d’obtenir des prédictions de l’abondance sur l’ensemble du cours d’eau, tout en prenant en compte les différentes sources d’incertitude.

Document Type: Research Article

Publication date: January 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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