1. Capture-recapture (CR) techniques are commonly used to gain information about population dynamics, demography and life-history traits of populations. However, traditional CR models cannot separate mortality from emigration. Recently developed spatial-capture-recapture (SCR) models explicitly incorporate spatial information into traditional CR models, thus allowing for individuals’ movements to be modelled explicitly. 2. In this paper, we extend SCR models using robust-design data to allow for both processes in which individuals can disappear from the population, mortality and dispersal, to be estimated separately. We formulate a general robust-design spatial capture-recapture (RD-SCR) model, explore the properties of the model in a simulation study, and compare the results to a Cormack-Jolly-Seber model and a non-spatial robust-design model with temporary emigration. In the case study, we fit several versions of the general model to data on field voles (Microtus agrestis) and compare the results with those from the non-spatial models fitted to the same data. We also evaluate assumptions of the fitted models with a series of simulation-based posterior predictive goodness-of-fit checks that are applicable to SCR models in general and the RD-SCR model in particular. 3. The simulation results show that the model preforms well under a wide range of dispersal distances. Our model outperforms the traditional CR models in terms of both accuracy and precision for survival. The case study showed that adult females have an approximately 3.5 times higher mortality rate than adult males. Males have larger home-ranges and disperse longer distances than females, but both males and females mostly move their activity centres within their previous home-range between trapping sessions at three week intervals. 4. Our RD-SCR model has several advantages compared to other approaches to estimate “true” survival instead of only “apparent” survival. Additionally, the model extracts information about space use and dispersal distributions that are relevant for behavioural studies as well as studies of life-history variation, population dynamics and management. The model can be widely applied due to the flexible framework, and other variations of the model could easily be implemented.