Résumé
To successfully perform their long‐distance migrations, migratory birds require sites along their migratory routes to rest and refuel. Monitoring the use of so‐called stopover and staging sites provides insights into (a) the timing of migration and (b) the importance of a site for migratory bird populations. A recently developed Bayesian superpopulation model that integrates mark–recapture data and ring density data enabled the estimation of stopover timing, duration, and population size. Yet, this model did not account for heterogeneity in encounter (p) and staying (ϕ) probabilities.Here we extended the integrated superpopulation model by implementing finite mixtures to account for heterogeneity in p and ϕ. We used simulations and real data (from 2009–2016) on red knots Calidris canutus, mostly of the subspecies piersmai, staging in Bohai Bay, China, during spring migration to (a) show the importance of accounting for heterogeneity in encounter and staying probabilities to get unbiased estimates of stopover timing, duration, and numbers of migratory birds at staging sites and (b) get accurate stopover parameter estimates for a migratory bird species at a key staging site that is threatened by habitat destruction.Our simulations confirmed that heterogeneity in p affected stopover parameter estimates more than heterogeneity in ϕ, especially when most birds had low p. Bias was particularly severe when most birds had both low ϕ and p. Bias was largest for population size, intermediate for stopover duration and negligible for stopover timing.A total of 50,000–100,000 red knots were estimated to annually stop for 5–9 days in Bohai Bay between 10 and 30 May. This shows the key importance of this staging site for this declining species. There were no clear changes in stopover parameters over time, although stopover population size was substantially lower in 2016 than in preceding years.Our study shows the importance of accounting for heterogeneity in both encounter and staying probabilities for accurately estimating stopover duration and population size and provides an appropriate modeling framework.