1992. Statistical models for the analysis of ageing error. Can. J. Fish. Aquat. Sci. 49: 1801-1 81 5.We present statistical models for estimating the true age distribution of a population, based on multiple readings from individual fish. There are two steps to this process. The first involves estimating a classification matrix that defines the probability of assigning an age a to a fish when its true age is b. Since true age is unknown, we require an assumption related to ageing error bias; we assume that the true age is the most probable value for the observed age. True age proportions, or alternatively, true ages of fish in the sample are then estimated in the second step. Our methods allow us either to conduct both steps simultaneously or to estimate true age proportions from a previously estimated classification matrix. We illustrate our methods with data on walleye pollock (Theragra chalcogramma). We recommend that multiple independent readings be obtained for a subset of structures in future ageing studies and that ageing error be considered in subsequent analyses. Sample sizes must be increased with increasing ageing error to achieve a specified precision in estimates of true age proportions.Nous presentons des modPles statistiques destines 2 estimer la distribution de I'ige reel d'une population 2 partir de mesures multiples sur des poissons pris individuellement. La demarche comporte deux etapes. La premiere est le calcul d'une matrice de classification qui definit la probabilite d'assigner u n 2ge a 2 un poisson tandis que son Age reel est b. Etant donne qu'on ne connait pas I'2ge reel, nous devons emettre une hypothese concernant I'erreur systematique dans la determination de I'2ge; nous posons que I'2ge reel est la valeur la plus probable de I'2ge observe. Dans la seconde etape, on estime les proportions des 2ges reels, ou encore I'2ge reel des poissons de I'echantillon. Nos methodes nous permettent soit d'effectuer les deux etapes simultanement soit d'estimer les proportions des iges reels 2 partir d'une matrice de classification calculee anterieurement. Nous illustrons nos methodes avec des donnees sur la goberge de I'Alaska (Theragra chalcogramma). Nous recommandons d'obtenir des mesures independantes multiples pour u n sous-ensemble de structures dans les etudes futures sur la determination de I'2ge, et de considerer dans les analyses subsequentes I'erreur dans la determination de I'dge. II est necessaire d'augmenter la taille des echantillons 2 mesure que I'erreur augmente pour arriver 2 une precision donnee dans I'estimation des proportions des ages reels. C atch-age data play a fundamental role in fish stock assessments (e.g. Megrey 1989) and in other studies of recruitment, growth, and mortality. Although many such analyses assume that fish ages are measured without error, this assumption is rarely met (Beamish and McFarlane 1983). Consequently, year-class strength can be incorrectly determined. Lai and Gunderson (1987) and Tyler et al. (1989) demonstrate how ageing error can lead to inappropr...

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This paper presents an analysis of stock–recruitment data that takes account of natural variation in stock productivity (process error) and inaccurate escapement counts (measurement error). We formulate the model using dynamic state variables and take advantage of related techniques for parameter estimation, such as an extended Kalman filter. Our recruitment function depends explicitly on parameters relevant to management and includes various cases of historical interest. We adopt Bayesian methods for assessing uncertainty and use Markov chain Monte Carlo (MCMC) techniques to obtain posterior samples. A worked example, based on simulated data, illustrates geometric relationships among model choices, estimated recruitment curves, and data interpretations.

Records of the date, location, and magnitude of Pacific herring (Clupea harengus pallasi) spawnings in British Columbia, collected since 1928, were compiled and analysed. In the early years of spawn surveys, adjacent spawnings were often reported as single events. Gradually, this practice has changed so that each spawning has a separate record. As a consequence of this change in methods, the mean length of spawnings has decreased in recent years but the total numbers of records has increased. Estimates of mean spawning width and intensity have also changed, partly due to changing survey methods as well as changing spawning distributions. A spawn abundance index is developed to account for these temporal changes. Abiotic factors affecting the distribution and abundance of spawn deposition include sea surface temperatures and the fishery. Biological factors affecting spawn distribution and abundance are not as well defined, but it is shown that in some situations, spawn dimensions may change as a function of stock abundance.

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