DOI: 10.5061/dryad.9s4mw6mmk
title: Migration strategies, performance and annual activity budget in a short-distance migrant, the common starling Sturnus vulgaris

description: Migratory birds typically separate energetically demanding parts of the
 annual cycle like breeding, moult, and migration with some species
 engaging in so-called moult-migration. Moult-migration is known to occur
 in starlings from the northern breeding populations, however, little is
 known about the dynamics of this phenomenon and the costs and benefits for
 the involved individuals. Here, using state-of-the-art multi-sensor
 geolocators, we gathered information about the annual cycles of 10
 starlings from two breeding sites in Latvia. We used a novel analytical
 approach based on atmospheric pressure measurements to reveal that all but
 one of the tracked individuals migrated to wintering sites in the British
 Isles. Tracking data exposed two separate migration strategies – (1)
 departure from the breeding grounds in mid-June soon after chick fledging
 with long stationary periods at moulting sites approx. 900 km westward (n
 = 5 of 10); (2) residing in close vicinity of the breeding sites up until
 the end of October (n = 5 of 10). Accelerometer data revealed
 significantly higher activity budgets during moult for the individuals
 exhibiting moult-migration. Furthermore, birds that underwent
 moult-migration arrived at the breeding sites in the following year on
 average 10 days later and showed significantly higher activity levels
 during the pre-breeding period compared to birds without moult-migration.
 Activity tracking also showed that 67% of all migratory flights were
 performed during the night, contradicting previous assumptions of
 starlings being predominantly diurnal migrants. Maximum recorded flight
 altitudes reached 2500 m.a.s.l. and the longest uninterrupted flight
 lasted 22.5 hours. Our results highlight energetic trade-offs of
 moult-migration in starling, but their downstream consequences remain to
 be tested.
description: In 2020, we set up two new
 study sites in Latvia to study Starlings. One in eastern Latvia in
 Zeltaleja (57.19°N 26.86°E) consisting of 100 nest boxes and the second
 in western Latvia near Lake Engure (57.27°N 23.10°E) consisting of two plots ca. 8 km apart with
 100 nest boxes each. The distance between the two study sites was 230 km.
 During the breeding season from mid-April until mid-June all nest boxes
 were monitored at least once every four days and adult birds and chicks
 were ringed. Onset of egg laying at both study sites was highly
 synchronized among individuals with 90% of all recorded broods (n = 181)
 started between 19 and 29 April. No second brood was recorded at either of
 the two study sites. In 2020, we fitted 70 adult breeders (35 at each
 site; 34 males, 36 females) with multi-sensor archival data loggers
 (model: GDL3-PAM, Swiss Ornithological Institute,  Liechti et al. 2018), which were
 attached on the bird's back using nylon cord leg-loop harness. Adult
 birds were predominantly captured with traps inside the nest boxes. To
 avoid nest abandonment, the trapping and deployment of loggers were
 carried out during the late stages of the breeding between 17 and 26 May
 when the chicks were more than 10 days old. The geolocator including the
 harness on average weighed 1.90 ± 0.04 g, which represents less than 3% of the
 body mass of starling (80.4 ± 3.93 g, n = 107), falling under recommended ethical threshold
 (Barron et al. 2010, Brlík et al.
 2020). The loggers accommodated sensors for ambient light
 intensity, atmospheric pressure, ambient temperature, and acceleration.
 The light sensor was equipped with a 7 mm long light guide and light
 intensity was measured every minute storing maximum values in 5 min
 intervals. Acceleration/activity was measured along the Z-axis for 3.2
 seconds at 10 Hz frequency every 5 minutes. Atmospheric pressure and
 temperature recordings were set to 30-minute intervals (Briedis et al.
 2020). In 2021, we acquired nine full tracks (5 males,
 4 females) and two incomplete tracks (1 male, 1 female). One of the
 incomplete tracks (male) had recorded data up until the 10th of December,
 thus, including information about the autumn migration and the first half
 of the wintering period, while the others (female) barometric pressure
 data were unusable due to malfunction of the sensor. Overall recapture
 rate for birds with loggers was 15.7% (11/70); 20% (7/35) in Engure, and
 11% (4/35) in Zeltaleja. To see whether loggers had any impact on return
 rates we used Chi-square test comparing return rates of logger birds with
 those of control groups (n = 18; birds undergoing the same handling
 procedure except fitting the loggers). Results showed no significant
 difference between the two groups (χ<sup>2</sup> = 0.02, p =
 0.88; males only: χ<sup>2</sup> = 4.77, p = 0.21; females only:
 χ<sup>2</sup> = 1.08, p = 0.84).
 For distinguishing between
 movement and stationary phases during the annual cycle, we used
 accelerometer and atmospheric pressure recordings. Because starlings use
 flapping flight for migration (Rayner et
 al. 2001), we used the flapping flight classification from
 the R-package ‘pamlr’ (Dhanjal-Adams et
 al. 2022) and set the duration threshold to 1 h (equals to 12
 consecutive readings of flapping activity at 5 min recording intervals).
 Thus, activity measures that were classified as flapping and lasted for at
 least 1 h were regarded as migratory flights (Briedis et al. 2020). Using 1 h as
 the cut-off threshold for the identification of migratory flights may have
 left some shorter duration flights unnoticed. However, lowering the
 threshold increases the risk of misidentifying extended commutes to and
 from roosting sites or lengthy murmurations (King and Sumpter 2012) as
 migration. From the classified migratory flights, we could invertedly
 identify stopover periods in-between migration flights. Longer periods
 exceeding 45 days were regarded as residency sites. We considered long
 stationary phases during winter months (November-February) as the main
 wintering grounds. To derive geographic locations of stationary
 sites, we used atmospheric pressure as it is not expected to change
 rapidly when the bird is stationary (weather-related changes; Liechti et al. 2018, Sjöberg et al.
 2018). Throughout the analyses, we followed the general
 procedure outlined in Nussbaumer et al. (2022a).
 The recorded
 pressure data were compared with surface-level atmospheric pressure data
 (ERA5 hourly surface-level pressure data) at 0.25x0.25-degree grid cells
 available at The Copernicus Climate Change Service (Hersbach et al. 2018).
 Based on pre-analyses of light data, we defined an area between
 60°N, 45°N, 10°W and 30°E where all birds had resided throughout the
 annual cycle and further location estimates were rendered in this
 pre-defined area to save computational time. To avoid birds’ behaviour
 (e.g., flying, foraging, etc.) potentially influencing the recorded
 pressure data, only recordings during night (light recording = 0) and
 while the bird was not in motion (acceleration &lt; 5 units on the
 arbitrary scale; max recorded value across all birds = 101) were used for
 location estimation. First, we excluded all the cells where recorded
 pressure data did not match the range of air pressure between lowest and
 highest elevation points within a cell at a given hour. To calculate this
 range, we combined remotely sensed elevation data at 90x90m resolution
 available from SRTM DEM Digital Elevation Database (Jarvis et al. 2008) and the ERA5
 data. Second, to further refine possible locations we calculated the
 maximal distance starlings could have travelled from the previous
 stopover/residency site assuming flight duration as per flight
 classification analyses and maximum average ground speed of 110km
 h<sup>-1</sup> (we deliberately chose a very high ground speed
 value to account for potential effects of strong tailwind support).
 Consequently, we excluded all cells outside the potential flight range.
 Third, we derived longitudinal boundaries of the stopover/residency sites
 from recorded light intensity data using R-package, ‘GeoLight’ (Lisovski et al. 2012). Longitude, in
 contrast to latitude, estimates are more reliable throughout the year as
 they are not influenced by calibration and equinox times (Lisovski et al. 2012). After
 integrating these steps, we ran a correlation analysis of pressure
 recorded on the geolocator and ERA5 data in all remaining grid cells.
 Higher correlation coefficient here indicates a higher likelihood that a
 bird was residing at a given grid cell. Finally, we checked residuals
 between the recorded pressure data from geolocators with the ERA5 data
 from the cell with the highest correlation coefficient. This procedure
 allowed for detection of changes in roosting sites and potential migratory
 flights that were shorter than 1 hour. If such flights were discovered, we
 split the stationary period accordingly and rerun the analyses (see
 section <em>Labelling tracks</em> from the user manual for
 GeoPressureR, Nussbaumer et al. 2022a, 2022b PREPRINT). Final maps
 illustrate grid cells with correlation coefficients within the 95th
 percentile (pressure data) of the potential residency/stopover
 area. Recorded light intensity data were used to
 identify sunrise and sunset times, which allowed us to distinguish between
 diurnal and nocturnal migratory flights. To better understand the
 migratory behaviour of starlings, this data was also used to compare
 departure and landing times to local sunrise and sunset. Also, individual
 migratory flight durations were compared with take-off times relative to
 local sunset. Further, for each individual, we calculated cumulative
 flight duration, migration distances, average ground speed and migration
 speed in summer/autumn and in spring. Migration distance was calculated as
 a great circle distance between the breeding site and the most likely
 location estimates across the annual cycle. Here, ‘migration speed’ (km
 day<sup>-</sup><sup>1</sup>) describes speed at which a bird travelled from
 breeding site to the main wintering grounds and back, including stopover
 time. ‘Ground speed’ (km h<sup>-1</sup>) describes flight speed between individual
 stationary points along the migratory track.
 Application of hypsometric
 equation to the atmospheric pressure measurements allowed us to calculate
 flight altitudes of each individual flight at 30 min temporal resolution
 (‘pamlr’ function calculate_altitude; Dhanjal-Adams et al.
 2022).
 Flight altitudes were
 later compared between autumn and spring, as well as between diurnal and
 nocturnal flights using a two-tailed Student's
 t-test.
description: R-package ‘pamlr’ (Dhanjal-Adams et al.
 2022)