Pasar al contenido principal

    Modelling the dynamics of krill populations in the Antarctic Peninsula region

    Solicitar acceso a documento de reunión
    Número de documento:
    WG-EMM-99/56
    Autor(es):
    E.J. Murphy (United Kingdom), A. Constable (Australia) and D. Agnew (United Kingdom)
    Punto(s) de la agenda
    Resumen

    The current long-term estimates of mean recruitment rates suggest that the population is unsustainable, as they are too low to maintain the estimated mortality rate. The variable annual estimates of recruitment to the population can be used to model in detail interannual variation in the population dynamics of krill and estimate the expected mortality rates. A number of models of the population dynamics of krill are used to assess to what extent they can explain the observed changes in the density of the population in the Antarctic Peninsula region. Two approaches have been explored: the first uses the bulk density estimates and uses a non-linear regression method to estimate the mortality rate. The second method develops a fully age-structured population model and uses only the recruitment data to develop a model of the long-term dynamics. Data on the recruitment of the first and second age groups were used to derive different estimates of mortality rates. Both model approaches applied to the recruitment data for the first age class produced an instantaneous mortality rate estimate of approximately 0.6 (?43% per annum). In both cases however the mortality rate estimate is poorly constrained in a range from about 0.3 to 1.0 (26%-63%) and the long-term trajectories of density estimated by the models give a relatively poor fit to the observed data. Using the recruitment data for the second age class produced higher mortality rate estimates of between 0.8-1.0 (59-63%) and produced better fits to the observed density changes. The need for caution in interpreting the model results was emphasized by an analysis of the sensitivity, which showed that the density data strongly constrain the model trajectories, which are less sensitive to changes in the recruitment rates.