AimContinental comparisons of migration strategies within and between populations of single species are rare but can be insightful to understand key environmental factors shaping differences in migration systems. We investigated differences in stopover networks and migration strategies between three separate Eurasia Greater White‐fronted Geese (GWFG) Anser albifrons populations to better understand how each overcomes the different topographic challenges faced along their migration routes during spring and autumn.LocationEurasia.TaxonBirds.MethodsUsing 106 (autumn) and 65 (spring) tracks from tagged GWFG from three Eurasian populations (Baltic‐North Sea [BNS] in the west; East Asia Continental [EAC] and West Pacific [WP] in the east), we generated stopover networks, calculated network metrics, quantified migration parameters and compared variation between populations and seasons.ResultsBNS showed largest network size, shortest average geodesic distance in both seasons, shortest migration distances, most stopover sites, longest stopover duration and shortest step length. EAC showed longest migration distance and second maximal flight length (>1600 km). WP showed shortest migration durations and longest maximal flight length (>2500 km). Summering ground arrival dates did not differ between populations. Autumn migration duration was shorter and migration speed faster than in spring in all populations.Main ConclusionsWe infer lack of obvious ecological barriers to BNS geese shapes their frequent stopovers of short duration. In contrast, EAC geese face two major ecological barriers (3100 km boreal forest, high mountains, dense human settlement and ocean) and WP geese must pass c. 2400 km of forest, mountains and ocean along their migration corridors, necessitating longer staging and migration segments of greater duration. We conclude that, despite almost identical body plan, all populations respond to radically different topographic challenges, adapting to meet these using different movement strategies, balancing migration schedule and fat accumulation patterns with availability and quality of stopovers.