Modelling individual heterogeneity in mark-recapture studies

Since its original derivation, researchers have developed different extensions of the Cormack-Jolly-Seber (CJS) model in order to accommodate variation in the survival and recapture probabilities across the population. More recent developments have allowed each of these probabilities to vary as a function of observable covariates. The relationship between the parameters and covariates is often expressed in the form of a logistic regression. Such regressions are useful because they reduce the overall number of parameters in the model and may offer important ecological insight into the survival and recapture processes. However, the use of covariates in mark-recapture studies also gives rise to two important problems: (1) the covariates may contain missing values; and (2) the covariates may be subject to measurement error. To date, the latter issue has only been addressed in the context of closed population models. In this thesis we demonstrate the effects of measurement error in the CJS model. More specifically, we consider the case where the survival probabilities are modelled as a logistic function of an error-prone time-varying covariate. The covariate is then subject to both missing values and measurement error. Although a conditional likelihood approach can be used to handle the missing values, the resulting model makes no provision for errors in the covariate. A simulation study shows that, when these errors are ignored, the regression coefficients are estimated with bias and the effect of the covariate is understated. Furthermore, the bias becomes more severe as the magnitude of the errors increases. To accommodate measurement error in the model, we use a refinement of the regression calibration (RRC) method, which is based on deriving an approximate model for the survival probabilities given the observed covariate values in terms of the true regression parameters.EThOS - Electronic Theses Online ServiceGBUnited Kingdo