Predictive distribution models generally use data from multiple individuals each associated with a unique observation location, coupled with environmental parameters, to define the overall suitable area where the species could occur. Whether the use of multiple locations from few individuals is similarly efficient to model species distribution is not yet known. If so, the method could bring valuable inputs to identify priority conservation areas for rare or elusive species, especially those large vertebrates tracked with telemetry. Satellite tracking of long-distance migrants often produces numerous locations of surveyed individuals across their wintering ranges. We examined such wintering tracks to test if the wintering locations obtained from few tracked individuals could be useful to model the full species wintering range, using habitat suitability models. We aimed at predicting the wintering distribution of West European Black Storks Ciconia nigra. We used tracks of 9 storks, retaining one location per winter day per individual for a total of 972 different locations. Combined with bioclimatic and land cover data in habitat suitability modelling within an ensemble forecast framework, we obtained a probabilistic distribution which largely matched the previously reported wintering range of the species in western and central Africa. Using tracks of the six individuals with more than 100 locations each, we obtained a distribution range closely matching the nine-bird range. A range similar to the nine-bird range could be predicted by models using data from less numerous individuals and by considering a lower threshold value. Habitat suitability models using wintering record locations of satellite-tracked migrant birds can therefore help to get a better picture of the wintering distribution range, and hence provide more accurate information for conservation planning on African wintering grounds.