Temporary migration - where individuals can leave and re-enter a sampled population - is a feature of many capture-mark-recapture (CMR) studies of mobile populations which, if unaccounted for, can lead to biased estimates of population capture probabilities and consequently biased estimates of population abundance. We present a method for incorporating radiotelemetry data within a CMR study to eliminate bias due to temporary migration using a Bayesian state-space model. Our results indicate that using a relatively small number of telemetry tags, it is possible to greatly reduce bias in estimates of capture probabilities using telemetry data to model transition probabilities in and out of the sampling area. In a capture-recapture data set for trout Cod in the Murray river, Australia, accounting for temporary migration led to overall higher estimates of capture probabilities than models assuming permanent or zero migration. Also, individual heterogeneity in detectability can be managed through explicit modelling. We show how accounting for temporary migration when estimating capture probabilities can be used to estimate the abundance and size distribution of a population as though it were closed. Our model provides a basis for more complex models that might integrate telemetry data into other CMR scenarios, thus allowing for greater precision in estimates of vital rates that might otherwise be biased by temporary migration. Our results highlight the importance of accounting for migration in survey design and parameter estimation, and the potential scope for supplementing large-scale CMR data sets with a subset of auxiliary data that provide information on processes that are hidden to primary sampling processes.