A hierarchical model is described for estimating population size from single-and multiple-pass removal sampling. The model is appropriate for two-stage sampling schemes, typified by surveys of riverine fish populations, in which multiple sites are surveyed, but a low number of passes are undertaken at each site. The model estimates the average population size within the target area from the raw catch data, and thus allows for differences in the sampling procedure at each site, such as including single-pass sampling. The model also uses the data from all sites to estimate the population size at each individual site. This results in generally improved precision for multiple-pass sites and provides comparable estimates from single-pass sites. A Bayesian approach is described for estimating the parameters of the hierarchical model using sampling importance resampling (SIR). An empirical Bayesian approach, which ignores prior uncertainty but is simpler to implement, is also described. Application of the hierarchical model is illustrated with electrofishing data for 0+ trout (Salmo trutta) in the River Inny, U.K.Résumé : On trouvera ici un modèle hiérarchique qui permet d'estimer la taille d'une population à partir d'un échantil-lonnage par retraits uniques ou répétés. Le modèle est approprié à un plan d'échantillonnage à deux étapes, comme, par exemple, dans les inventaires de poissons de rivière où de nombreux sites sont inventoriés, mais avec un petit nombre d'échantillonages à chaque site. Le modèle estime la taille moyenne de la population dans la zone ciblée à partir des données brutes de capture et ainsi s'accommode de différences de stratégie d'échantillonnage d'un site à l'autre et peut, en particulier, inclure les données d'un échantillonnage unique. Le modèle utilise aussi les données recueillies à tous les sites pour estimer la taille de la population à chacun des sites. Il en résulte généralement une précision accrue pour les sites échantillonnés à plusieurs reprises et une précision comparable pour les sites échantillonnés une seule fois. Une approche bayésienne, décrite ici, permet d'estimer les paramètres du modèle hiérarchique à l'aide d'un échantillonnage d'importance et d'un rééchantillonnage (modélisation SIR). On trouvera aussi une approche empirique bayésienne qui ignore l'incertitude a priori et qui est plus simple à utiliser. L'étude de données obtenues par pêche électrique sur des truites (Salmo trutta) de l'Inny, au Royaume-Uni, illustre le genre d'application qu'on peut faire du modèle hiérarchique.[Traduit par la Rédaction] Wyatt 706

A hierarchical Bayesian model is described for mapping the abundance of fish throughout a watershed from single- and multiple-pass removal sampling. A geographic information system (GIS) was used to generate a raster-based model of the river network, which provided three benefits for estimating fish density. Firstly, the horizontal resolution of the raster (50 m) provided an approximation to the statistical sampling frame and allowed correction for the finite number of potential sampling sites in a reach. Secondly, the modelled river network generated explanatory variables for every site in the network, facilitating the mapping of predicted densities and providing the basis for stratified or regression estimators for reach-specific densities. Finally, the spatial autocorrelation of fish densities was modelled in terms of the distance along the river network. A similar Bayesian model was also developed for the wetted width of the river network, and this was combined with the density model to provide estimates of the total stock size. The model was implemented using Markov chain Monte Carlo simulation in the statistical package WinBUGS. Application of the hierarchical model is illustrated with electrofishing data for age-1+ sea trout (Salmo trutta) in a small tributary of the River Conwy, Wales.

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The effective management of salmonid fisheries requires that the factors influencing variation in the abundance of stream populations are understood. The use of habitat models to explain the spatial component of population variance offers potential for management, but has not previously been set in the context of long term variation in population abundance because of the lack of suitable data sets. This paper examines contributions of spatial and temporal factors lo fish density variance using a 10‐year data set from five tributaries of the River Conwy, North Wales. Recently developed habitat models were applied to the data to test their ability to explain nominal spatial variance. Spatial variance accounted for between 21 and 62% of the overall variance of salmonid abundance, and habitat models explained up to 95% of the spatial variance component. Synchrony in population variation amongst sites within and between tributaries is described, and some of the factors that may influence this are discussed.

Receivcd April 24, I