Although population mixtures often include contributions from novel populations as well as from baseline populations previously sampled, unlabeled mixture individuals can be separated to their sources from genetic data. A Gibbs and split-merge Markov chain Monte Carlo sampler is described for successively partitioning a genetic mixture sample into plausible subsets of individuals from each of the baseline and extra-baseline populations present. The subsets are selected to satisfy the Hardy-Weinberg and linkage equilibrium conditions expected for large, panmictic populations. The number of populations present can be inferred from the distribution for counts of subsets per partition drawn by the sampler. To further summarize the sampler's output, co-assignment probabilities of mixture individuals to the same subsets are computed from the partitions and are used to construct a binary tree of their relatedness. The tree graphically displays the clusters of mixture individuals together with a quantitative measure of the evidence supporting their various separate and common sources. The methodology is applied to several simulated and real data sets to illustrate its use and demonstrate the sampler's superior performance.Résumé : Bien que les mélanges de populations contiennent des contributions des nouvelles populations en plus de celles des populations originales déjà échantillonnées, les individus non identifiés du mélange peuvent être séparés en fonction de leur source par leurs caractéristiques génétiques. Nous décrivons un échantillonneur de Gibbs de type Monte Carlo avec procédure de séparation-regroupement par chaînes de Markov qui sépare successivement un échantil-lon contenant un mélange génétique en sous-ensembles plausibles d'individus à la fois de la population d'origine et des populations additionnelles présentes. Les sous-ensembles sont sélectionnés de manière à satisfaire aux exigences de l'équilibre Hardy-Weinberg et de l'équilibre de liaison attendus dans de grandes populations panmictiques. Le nombre de populations présentes peut être estimé à partir de la distribution des nombres de sous-ensembles par partition retirés par l'échantillonneur. Afin de mieux résumer le produit de l'échantillonneur, les probabilités d'attribution conjointe des individus du mélange aux mêmes sous-ensembles sont calculées à partir des partitions et elles servent à construire un arbre binaire de leur degré de parenté. L'arbre représente graphiquement les groupements d'individus du mélange de même qu'une mesure quantitative des données qui appuient leurs différentes sources séparées et communes. Nous appliquons la méthodologie à plusieurs ensembles de données réelles et simulées afin d'en illustrer l'utilisation et de démontrer la performance supérieure de cet échantillonneur.[Traduit par la Rédaction] Pella and Masuda 596

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Mixture modeling is shown to outperform classical individual assignments for both estimating stock composition and identifying individuals' sources in a case study of an eight-locus DNA microsatellite database from 26 Atlantic salmon (Salmo salar) stocks of the Baltic Sea. Performance of the estimation methods was compared using selfassignment tests applied to each of the baseline samples and using independent repeat samples from two of the baseline stocks. The different theoretical underpinnings, hypothesis testing versus decision theory, of the methods explain their estimation capacities. In addition, actual catch samples from three northern Baltic Sea sites in 2000 were analysed by mixture modeling, and estimated compositions were consistent with previous knowledge. Baltic main basin and Gulf of Finland stocks were each minor components (<1% at any site), and three groups of Gulf of Bothnia stocks, wild (36%-43% among sites), Finnish hatchery (15%-49%), and Swedish hatchery (11%-41%), were each important with the two hatchery contributions trending geographically.Résumé : Dans une étude de cas comportant une banque de données sur des microsatellites ADN à huit locus provenant de 26 stocks de saumons atlantiques (Salmo salar) de la Baltique, nous montrons qu'un modèle de mélange fonctionne mieux que les assignations individuelles classiques, tant pour estimer la composition des stocks que pour identifier les origines des individus. Nous avons comparé la performance des méthodes d'estimation à celle des tests d'auto-assignation appliqués à chacun des échantillons de base et à des échantillons répétées indépendants des deux stocks de base. Les différents fondements théoriques, évaluation d'hypothèse ou théorie de décision, des méthodes expliquent leur capacité d'estimation. De plus, nous avons analysé des échantillons réels de récoltes en 2000 dans trois sites du nord de la Baltique à l'aide de modèles de mélange; les compositions estimées que nous avons obtenues concordent avec nos connaissances antérieures. Les stocks du bassin principal de la Baltique et du golfe de Finlande sont tous deux des composantes mineures (<1 % à tous les sites) et trois groupes de stocks du golfe de Bothnie, stocks sauvages (36-43 % parmi les sites), stocks finlandais de pisciculture (15-49 %) et stocks suédois de pisciculture (11-41 %), sont tous importants; les deux contributions des piscicultures suivent des gradients géographiques.[Traduit par la Rédaction] Koljonen et al. 2158

Chinook Salmon Oncorhynchus tshawytscha returns to the Yukon River basin have declined dramatically since the late 1990s, and detailed information on the spawning distribution, stock structure, and stock timing is needed to better manage the run and facilitate conservation efforts. A total of 2,860 fish were radio‐tagged in the lower basin during 2002–2004 and tracked upriver. Fish traveled to spawning areas throughout the basin, ranging from several hundred to over 3,000 km from the tagging site. Similar distribution patterns were observed across years, suggesting that the major components of the run were identified. Daily and seasonal composition estimates were calculated for the component stocks. The run was dominated by two regional components comprising over 70% of the return. Substantially fewer fish returned to other areas, ranging from 2% to 9% of the return, but their collective contribution was appreciable. Most regional components consisted of several principal stocks and a number of small, spatially isolated populations. Regional and stock composition estimates were similar across years even though differences in run abundance were reported, suggesting that the differences in abundance were not related to regional or stock‐specific variability. Run timing was relatively compressed compared with that in rivers in the southern portion of the species’ range. Most stocks passed through the lower river over a 6‐week period, ranging in duration from 16 to 38 d. Run timing was similar for middle‐ and upper‐basin stocks, limiting the use of timing information for management. The lower‐basin stocks were primarily later‐run fish. Although differences were observed, there was general agreement between our composition and timing estimates and those from other assessment projects within the basin, suggesting that the telemetry‐based estimates provided a plausible approximation of the return. However, the short duration of the run, complex stock structure, and similar stock timing complicate management of Yukon River returns. Received March 5, 2014; accepted August 27, 2014