Swift movements were categorised as migration (corridor movements) and stationary (area-restricted search) using a two-state Hidden Markov Model (McClintock & Michelot 2018, HMM), with missing positions filled using continuous-time correlated random walk data (Johnson et al. 2008) to create a homogeneous six-hour time series. Step distances (i.e. Euclidean distances between subsequent GPS positions) were modelled using a gamma distribution, while turning angle distributions followed a von Mises distribution (initial parameters; gamma distributions: μf = 50 km, σf = 10 km; μm = 80 km, σm = 10 km; von Mises distributions: μf = 0, κf = 1; μm = 0, κm = 2, migration and foraging denoted with subscripts m and f, respectively). State classes (i.e. migration, stationary) were returned using global encoding with the Viterbi algorithm (Zucchini et al. 2016, McClintock & Michelot 2018).
Atmospheric pressure recorded was converted to altitude (z) using the international standard atmosphere which is defined as:
\[ z = \frac{T_0}{L}*\left({\frac{P_0}{P}}^{\frac{LR_0}{g}} - 1\right)\]
With T0 the temperature at sea level (288.15 K), L the temperature lapse rate (-0.0065° K m-1), P0 the standard atmospheric pressure at sea level (1013.25 hPa), P the measured air pressure (hPa), g the gravitational acceleration (9.81 m s-1) and R0 the universal gas constant (287.053 J kg-1 K-1).
Additional summary statistics to those described in the main manuscript are provided. We report mean average flight altitude and height, activity with their uncertainty (on standard deviation). Furthermore we tabulate current and/or long term swift recapture rates.