DOI: 10.5061/dryad.2z34tmpwr
title: Route flexibility is associated with headwind minimization in a long-distance migratory seabird

description: Seasonal migration has evolved across taxa and encompasses a multitude of
 features, many of which vary between species, between and within
 populations, and even within individuals. One feature of migration that
 appears especially variable within individuals is the route taken to reach
 a destination, even when the destination itself is not variable at this
 level. To investigate why, we analysed the geolocator tracks describing
 192 post-breeding migratory journeys of 84 common terns (Sterna hirundo),
 as well as 149 pre-breeding migratory journeys of 75 of these birds. We
 found little within-individual spatial consistency in migration routes
 across years, irrespective of season or sex. Instead, individuals
 departing during the same time window took similar migration routes,
 which, during pre-breeding migration, when birds predominantly encountered
 headwinds, were associated with minimised headwind exposure. We therefore
 suggest that the individual routes of this long-distance migratory seabird
 can be flexibly adjusted to environmental variation, which is likely to be
 adaptive.
description: Between mid-May and early July 2016 – 2020, we caught 84
 incubating common terns a total of 229 times with a drop trap, on average
 16 ± 4.3 SD days after the first egg in their nest was laid. These birds
 were then equipped with a geolocator attached to the leg using a 10 mm
 aluminium ring [Kürten et al. 2019]. The geolocators were set to sample
 ambient light intensity every minute, with the maximum light intensity
 being stored every five minutes (mode 10). The total mass of the
 geolocator, ring and glue was 1.6 g, which equalled 1.2% ± 0.1 SD of the
 body mass of the birds at capture (mean body mass: 129.1 g ± 8.1 SD). In
 the breeding seasons of 2017 – 2021, we re-trapped the 84 individuals
 equipped with a geolocator that returned (i.e. 86%) a total of 198 times,
 and recovered 192 devices with light-level data (i.e. 97%). As such, we
 obtained data for 192 post-breeding migratory journeys performed by 84
 individual birds (41 males and 43 females), as well as 149 pre-breeding
 migratory journeys performed by 75 of these birds. To
 analyse the light-level data, we used the function “preprocessLight” of
 the R package “BAStag” [Wotherspoon et al. 2016] and a threshold of 1.5 to
 identify twilights. If visual inspection suggested extreme outliers
 (&gt;30 min difference with the previous and subsequent twilight), we
 either adjusted the outlying twilight or excluded it (c. 1% and &lt;
 1%, respectively; as calculated based on the light-level datasets of 10
 randomly selected individuals). All geolocators were
 calibrated ‘on-bird’ while the birds were at the Banter See colony (i.e.
 for multiple weeks). The start and end of the calibration periods were
 individually determined by visual inspection of the calibration slopes for
 sunsets and sunrises plotted by the “plot_slopes_by_location” function of
 the R package “FLightR” (version 0.5.0, [Rakhimberdiev et al. 2017]). As
 long as the bird is staying in the calibration location, these slopes vary
 very little [Rakhimberdiev et al. 2017], so that the first calibration
 period ended with a visual detection of a change in the slope (i.e. when
 the bird was assumed to have departed). For geolocators that where still
 working at recapture, a second calibration period was set to start when
 the slope did not show variation anymore (i.e. when the bird had
 arrived). For the subsequent movement analyses, we set
 the mean flight distance between two twilights to 1200 km, i.e. 24 hours x
 50 km/h [Bruderer & Boldt 2001], while allowing for ± 300 km and
 ran the particle filter with 1 million particles to optimise the track of
 each individual and to minimise its uncertainty [Rakhimberdiev et al.
 2015]. The uncertainty of location estimates obtained with “FLightR” is
 approximately 250 km per location (dependent on shading caused by the
 species’ behaviour), which is substantially less than that of estimates
 obtained with more traditional threshold methods [Rakhimberdiev et al.
 2017, Halpin et al. 2021]. We deem this uncertainty to be unlikely to
 impact our analyses substantially, since all tracks are expected to be
 equally affected, common terns migrate a distance (5,000 – 11,000 km;
 [Kürten et a. 2022]) that is relatively large compared to the uncertainty
 in the location estimate, and our large sample size (n = 192 post-breeding
 and 149 pre-breeding migratory journeys) is securing statistical
 power. Finally, we applied the
 stationary.migration.summary” function of “FLightR” with a “min.stay” of
 ten twilights (i.e. 5 days) and a “prob.cutoff” set to 0.4 (for detailed
 testing and explanation of these settings, see [Kürten et al. 2022]) to
 derive the longitude and latitude of each non-breeding residence area, as
 well as the dates of arrival to, and departure from, the breeding colony
 and each non-breeding residence area for each track from each bird. For
 192 tracks from 84 individuals, we obtained a single non-breeding
 residence area, whereas for 7 tracks of 6 individuals we obtained two. The
 post- and pre-breeding migration routes were obtained from the daily
 location estimates between the departure dates from, and arrival dates to,
 the breeding colony and first and last non-breeding residence area,
 respectively.
description: # Route flexibility is associated with headwind minimization in a
 long-distance migratory seabird
 [https://doi.org/10.5061/dryad.2z34tmpwr](https://doi.org/10.5061/dryad.2z34tmpwr) ## Description of the data and file structure Daily position data for our multi-year individual-based tracking study were collected in a common tern colony located at the Banter See in Wilhelmshaven at the German North Sea coast (53°30’40” N, 08°06’20” E). The data were used to test (i) whether birds show within-individual spatial similarity in the migration routes taken across years, and (i) to assess whether birds leaving within the same time window (and hence experiencing similar environmental conditions) take similar migration routes, and, if so, (iii) whether such among-individual temporal route similarity could be associated with minimisation of the encounter of unfavourable wind conditions. Contact Nathalie Kürten ([nathalie.kuerten@ifv-vogelwarte.de](mailto:hahnp@ufl.edu)) with any questions. This manuscript has been accepted for publication (18/02/2025): Kürten N, Wynn J, Haest B, Schmaljohann H, Vedder O, González-Solís J, Bouwhuis S. Route flexibility is associated with headwind minimization in a long-distance migratory seabird. Proc B. [http://doi.org/10.1098/rspb.2016.0049](http://doi.org/10.1098/rspb.2016.0049) R script for geolocator track analyses to extract daily positions can be found in the related publication Kürten et al. (2022) High individual repeatability of the migratory behaviour of a long-distance migratory seabird. Mov Ecol 10, 5. [https://doi.org/10.1186/s40462-022-00303-y](https://doi.org/10.1186/s40462-022-00303-y) (Additional file 2: [https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-022-00303-y#Sec17](https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-022-00303-y#Sec17)) R scripts (file: R_code_NND_analyses.R and R_code_wind_analyses.R) used to analyse the daily position data for these publication are included here (all needed R packages are listed in the R codes and both codes include annotations). ### Files and variables Daily position data file: #### File: data\_for\_Route\_flexibility\_is\_associated\_with\_headwind\_minimization\_in\_a\_long-distance\_migratory\_seabird.csv **Description:** This file contains daily position data of geolocator tracked common terns. ##### Variables * track_id: A unique number for each gelocator track * year: Year of geolocator deployment * geo: Geolocator ID * age: Age of first deployment with a geolocator * sex: Sex of the individual (m = male, f = female) * breeding_success: Breeding success of an individual in the year of deployment (1 = yes, 0 = no) * departure_colony: Departure date (dd.mm.yyyy) of the geolocator tracked individual from the breeding colony * arrival_wa_1: Arrival date (dd.mm.yyyy) of the geolocator tracked individual at its first wintering area * departure_wa_final: Departure date (dd.mm.yyyy) of the geolocator tracked individual at its final wintering area * arrival_date_geo: Arrival date (dd.mm.yyyy) of the geolocator tracked individual at the breeding colony * position_date_time: Day and time (dd.mm.yyyy hh:mm:ss) of the location of the tracked individual * position_lon: Longitude of the position of the tracked individual * position_lat: Latitude of the position of the tracked individual Note: The variable "name of the individual" was removed from the dataset to protect participant identity.