The Analysis of Ring-Recovery Data Using Random Effects

摘要: We show how random terms, describing both yearly variation and overdispersion, can easily be incorporated into models for mark-recovery data, through the use of Bayesian methods. For recovery data on lapwings, we that incorporation terms greatly improves goodness fit. Omitting lead to overestimation significance weather survival, overoptimistic prediction intervals in simulations future population behavior. Random effects provide a natural way modeling overdispersion-which is more satisfactory than standard classical approach scaling up all errors by uniform inflation factor. compare means p-values deviance information criterion (DIC).