Efficient capture-recapture inference for spatially varying natal dispersal,survival and recruitment

1. Natal dispersal is a key process in population ecology because it links local demographic processes to broader-scale population dynamics by redistributing individuals. When using capture-recapture data, multistate capture-recapture models using discrete spatial units as states are the gold standard for estimating natal dispersal among spatial units while accounting for spatial variation in survival, recruitment and imperfect detection. However, because their computational cost increases rapidly with the number of spatial units, applications have been limited to a small number of units. Therefore, in practice, these models cannot provide spatially detailed inference on natal dispersal across large landscapes. 2. We develop a computationally efficient Bayesian capture-recapture model, called the efficient natal dispersal (END) model, to estimate natal dispersal among discrete spatial units jointly with spatial variation in demographic parameters and detection probabilities. The END model relies on two key structural features: juveniles and breeders are separated into two arrays, and resightings outside the natal spatial unit are aggregated over time for individuals released as juveniles. 3. Using simulations, we show that the END model is considerably (up to 30 times) more computationally efficient than a conventional multistate model, while maintaining comparable parameter accuracy. We then apply the END model to white stork (Ciconia ciconia) capture-recapture data from Germany across 101 hexagonal spatial units, a spatial resolution at which a conventional multistate model is computationally infeasible. We estimate natal dispersal among units jointly with spatial variation in survival and recruitment. This allows us to identify areas of lower or higher survival, earlier or delayed recruitment, and dispersal probabilities among all units. By combining estimated dispersal probabilities with existing data on the number of juveniles born in each spatial unit, we estimate natal dispersal in terms of numbers of individuals and identify units with positive or negative net migration, sources and sinks. 4. Overall, our approach moves capture-recapture analyses from estimating natal dispersal among a few spatial units to inferring dispersal networks and assessing their demographic consequences across large domains. Our approach is applicable to many spatially structured capture-recapture datasets, opening new opportunities for studying spatial population dynamics.