Long-distance migratory shorebirds provide an ideal system for studying how animals adaptmovement decisions to rapidly changing environmental conditions across continental scales.In the East Asian-Australasian Flyway, changing coastal staging habitats and climate-drivenchanges in Arctic breeding regions may alter migration strategies, timing, seasonalconnectivity and movement networks. Forecasting the resilience of such migrations requirespredictive models that link environmental change to individual decisions across chains ofconnected habitats. Yet the relative importance of underlying assumptions andparameterisations remains uncertain across seasons and species. Building on an existingstate-dependent model of optimal northward migration, we implemented northward andsouthward migration scenarios to evaluate how alternative ecological assumptions shapepredicted movement dynamics across the annual cycle. For seven shorebird species, wesimulated historical (1970s), current (2010s) and future (2060s) habitat scenarios to assesshow environmental change affects migration timing, route choice and stopover-use strategies,while systematically varying parameterisations of predation risk, site quality, species-specificflight and fuelling parameters, and seasonal optimization criteria. By comparing modelpredictions with empirical tracks, we assessed which ecological assumptions were mostconsistent with observed movements, where model-track discrepancies revealed keyknowledge gaps and which behavioural adjustments may be required under environmentalchange. Southward migration could not be reproduced by simply reversing the spring model,but required a distinct survival-based optimization criterion, resulting in altered migrationtiming, stopover-use patterns and more spatially distributed movement networks. Together,these results demonstrate how predictive modelling frameworks can reveal the processesshaping movement dynamics across the annual cycle, while identifying where better data onspatio-temporal resource and risk landscapes, species traits and seasonal decision rules areneeded to improve year-round migration predictions.