1. The dynamics of many populations is strongly affected by immigrants. However, estimating and modelling immigration is a real challenge. In the past, several methods have been developed to estimate immigration rate but they either require strong assumptions or combine in a piecewise manner the results from separate analyses. In most methods the effects of covariates cannot be modelled formally.
2. We developed a Bayesian integrated population model which combines capture–recapture data, population counts and information on reproductive success into a single model that estimates and models immigration rate, while directly assessing the impact of environmental covariates.
3. We assessed parameter identifiability by comparing posterior distributions of immigration rates under varying priors, and illustrated the application of the model with long term demographic data of a little owl Athene noctua population from Southern Germany. We further assessed the impact of environmental covariates on immigration.
4. The resulting posterior distributions were insensitive to different prior distributions and dominated by the observed data, indicating that the immigration rate was identifiable. Average yearly immigration into the little owl population was 0·293 (95% credible interval 0·183–0·418), which means that ca 0·3 female per resident female entered the population every year. Immigration rate tended to increase with increasing abundance of voles, the main prey of little owls.
5.Synthesis and applications. The means to estimate and model immigration is an important step towards a better understanding of the dynamics of geographically open populations. The demographic estimates obtained from the developed integrated population model facilitate population diagnoses and can be used to assess population viability. The structural flexibility of the model should constitute a useful tool for wildlife managers and conservation ecologists.