Wind-assisted sprint migration in northern swifts

Abstract

Long-distance migration has evolved repeatedly in animals and covers substantial distances across the globe. The overall speed of migration in birds is determined by fueling rate at stopover, flight speed, power consumption during flight, and wind support. The highest speeds (500 km/day) have been predicted in small birds with a fly-and-forage strategy, such as swallows and swifts. Here, we use GLS tracking data for common swifts breeding in the northern part of the European range to study seasonal migration strategies and overall migration speeds. The data reveal estimated overall migration speeds substantially higher (average: 570 km/day; maximum: 832 km/day over 9 days) than predicted for swifts. In spring, swift routes provided 20% higher tailwind support than in autumn. Sustained migration speeds of this magnitude can only be achieved in small birds by a combined strategy including high fueling rate at stopover, fly-and-forage during migration, and selective use of tailwinds.

Foraging on the wing for fish while migrating over changing landscapes: traveling behaviors vary with available aquatic habitat for Caspian terns

“…Notably, when crossing the Sahara Desert and the Mediterranean Sea the birds adopted a “sprinting” migration with increased travel speeds, flight heights and daily flight hours, resulting in a mixed migratory behavior that varies with available habitat [ 26 ] and combines foraging along the route with stopovers in freshwater and coastal ecosystems [ 34 ]. Mixed strategies that include both stopovers and fly-and-forage have been previously recognized for a handful of species including ospreys [ 26 , 27 ], Eleonora’s falcons [ 61 ], lesser black-backed gulls [ 30 ], Cory’s shearwaters [ 62 ], common swifts [ 63 , 64 ] and bank swallows [ 65 ]. We foresee that more fly-and-forage migrants will be described as tracking technologies advance and continue to give new insights into the behavior of migratory birds.…”

A 30,000-km journey by Apus apus pekinensis tracks arid lands between northern China and south-western Africa

“…The light-level data were analysed using GeoLight 2.0.0 [ 35 ], an easy-to-adjust, intuitive and easy-to interpret package in R 4.0.2 [ 36 ]. More importantly, it allows comparisons of results generated with other packages in similar principals [ 19 , 21 , 37 ]. We set the threshold to 2 for log-transformed light-level data to identify the twilight events (dawn-sunrise and sunset-dusk) minimizing the latitude variation around the equinox [ 19 , 20 , 22 ].…”

“…The movement state (i.e. stationary, directed flight) during these periods was estimated by changes in longitude and positions before and after [ 20 , 37 ]. The final position data were imported into QGIS 3.14 for further analysis.…”

“…To avoid the inaccuracy of some positions caused by the occasional abnormal twilight events, we carried out manual correction for outliers with QGIS . Since the reported travel speed of Common Swift was above 250 km/day, and the stopovers below 2 days were considered indistinguishable from slow movement due to the inaccurate positioning [ 19 , 21 , 37 ], locations changing less than 500 km in at least 3 days were grouped as ‘stopover sites’ in our study. Then we selected the first position after the swifts reaching a stopover site to define the arrival date, and the last point before they leaving to define the departure date, and calculated the stopover time from that.…”

“…We compared the sexual and seasonal variations of migration parameters using t-tests. In addition, we compared the migration phenology of two groups of individuals from different subspecies, using a detailed set of published data from Sweden (N = 25) [ 19 , 37 ],representing northernmost populations of apus . In addition, we also compared our data with some general information on movement patterns from several European populations of apus in published studies which also used farthest sites as migration endpoints [ 22 ].…”