Résumé
M ultistate capture-recapture models are a natural generalization of the usual one-site recapture models. Similarly, individuals are sampled on discrete occasions, at which they may be captured or not. However, contrar y to the one-site case, the individuals can move within a ® nite set of states between occasions. The growing interest in spatial aspects of population dynamics presently contributes to making multistate models a very promising tool for population biology. We review ® rst the interest and the potential of multistate models, in particular when they are used with individual states as well as geog raphical sites. M ultistate models indeed constitute canonical capture-recapture models for individual categorical covariates chang ing over time, and can be linked to longitudinal studies with missing data and models such as hidden M arkov chains. M ultistate models also provide a promising tool for handling heterogeneity of capture, provided states related to capturability can be de® ned and used. Such an approach could be relevant for population size estimation in closed populations. M ultistate models also constitute a natural framework for mixtures of information in individual histor y data. Presently, most models can be ® t using program M ARK. As an example, we present a canonical model for multisite accession to reproduction, which fully generalizes a classical one-site model. In the generalization proposed, one can estimate simultaneously age-dependent rates of accession to reproduction, natal and breeding dispersal. Finally, we discuss further generalizationsÐ such as a multistate generalization of g rowth rate models and models for data where the state in which an individual is detected is known with uncertaintyÐ and prospects for software development.