Context: High-resolution animal movement data are becoming increasingly available, yet having a multitude of trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species' potential to act as a link between patches, populations, or ecosystems are of importance. Objectives: We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of their ecological likelihood. With this approach, we estimated connectivity at the landscape level throughout the annual cycle of bar-headed geese (Anser indicus) in its native range. Methods: We used a tracking dataset of bar-headed geese to parameterise a multi-state movement model and to estimate temporally explicit habitat suitability within the species' range. We simulated migratory movements between range fragments, and estimated their ecological likelihood. The results are compared to expectations derived from published literature. Results: Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The estimated likelihood of simulated migrations was similar to that of empirical trajectories. We found that the predicted connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species. Conclusions: We show how empirical tracking data and environmental information can be fused to make meaningful predictions about future animal movements. These are temporally explicit and transferable even outside the spatial range of the available data. Our integrative framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.