Title: "The interplay of wind and uplift facilitates over-water flight in facultative soaring birds" Abstract: Flying over the open sea is energetically costly for terrestrial birds. Despite this, over-water journeys of many birds, sometimes hundreds of kilometers long, are uncovered by bio-logging technology. To understand how these birds afford their flights over the open sea, we investigated the role of atmospheric conditions, specifically wind and uplift, in subsidizing over-water flight at the global scale. We first established that ∆T, the temperature difference between sea surface and air, is a meaningful proxy for uplift over water. Using this proxy, we showed that the spatio-temporal patterns of sea-crossing in terrestrial migratory birds is associated with favorable uplift conditions. We then analyzed route selection over the open sea for five facultative soaring species, representing all major migratory flyways. The birds maximized wind support when selecting their sea-crossing routes and selected higher uplift when suitable wind support was available. They also preferred routes with low long-term uncertainty in wind conditions. Our findings suggest that, in addition to wind, uplift may play a key role in the energy seascape for bird migration that in turn determines strategies and associated costs for birds crossing ecological barriers such as the open sea. Data overview: #Data files are used as inputs for the R scripts in https://github.com/mahle68/global_seascape_public for reproducing the results of the study. #Annotated temperature points used for generating regional energy seascapes: input_regional_gams.RData #output of regional GAMs used for plotting energy seascapes in Fig. 2: models_ls_reg_gams.RData #Data used for generating alternative steps: move_ls.RData #and the step selection function analysis: annotated_steps.RData #INLA model resulting from the step selection analysis: INLA_model.RData #new data generated for making predictions using the INLA model (to plot Fig. 4): new_data_for_modeling.RData #INLA model including these predictions: INLA_model_preds.RData #Other data files are mainly used for plotting. Please see all_figures.R in the Github repository for more information. #for any questions, contact enourani@ab.mpg.de