State-space modelling of data on marked individuals

State-space models have recently been proposed as a convenient and flexible framework for specifying stochastic models for the dynamics of wild animal populations. Here we focus on the modelling of data on marked individuals which is frequently used in order to estimate demographic parameters while accounting for imperfect detectability. We show how usual models to deal with capture–recapture and ring-recovery data can be fruitfully written as state-space models. An illustration is given using real data and a Bayesian approach using MCMC methods is implemented to estimate the parameters. Eventually, we discuss future developments that may be facilitated by the SSM formulation.