DOI: 10.5061/dryad.44j0zpck5
title: Data from: seasonal patterns and processes of migration in a long-distance migratory bird: energy or time minimization?

description: Optimal migration theory prescribes adaptive strategies of energy, time or
 mortality minimization. To test alternative hypotheses of energy and time
 minimization migration we used multisensory data loggers recording
 time-resolved flight activity and light for positioning by geolocation in
 a long-distance migratory shorebird, little ringed plover Charadrius
 dubius. We could reject the hypothesis of energy minimization based on a
 relationship between stopover duration and subsequent flight time as
 predicted for a time minimizer. We found seasonally diverging slopes
 between stopover and flight durations in relation to the progress (time)
 of migration, which follows for a time minimizing policy if resource
 gradients increase and decrease, respectively. Total flight duration did
 not differ significantly between autumn and spring migration, although
 spring migration was 6% shorter. Overall duration of autumn migration was
 longer than that in spring, mainly due to a mid-migration stop in most
 birds, when they likely initiated moult. Overall migration speed was not
 significantly different between autumn and spring. Migratory flights often
 occurred as runs of 2-7 nocturnal flights on adjacent days, which may be
 countering a time minimization strategy. Other factors may influence a
 preference for nocturnal migration, such as avoiding flight in turbulent
 conditions, heat stress, and diurnal predators.
description: The data were collected using custom designed micro data loggers
 (MDLs) that recored acceleration, light during specified periods, air
 pressure, and temperature. The loggers were deplyed on little ringed
 plovers Charadrisu dubois, during the breeding season (May and June) in
 southern Sweden, and retrived 1-2 years later.  The MDLs
 were pre-programmed with a calendar defining when to run the light level
 measurements for position estimates. This approach is different from
 conventional geolocators in that our loggers only measured sequences of
 daily light cycles for a limited number of consecutive days. The reduction
 of measurement periods of light-level substantially reduces the amount of
 data to be stored and prolongs operation time by minimizing the power
 consumption. In our study, we ran six measurement periods each of 5 days
 distributed over one year, starting on 15 July, 1 September, 1 November, 1
 February, 15 April, and 5 May. The timing of measurement sequences was
 selected to match periods of residence previously identified by
 conventional geolocators (Hedenström et al. 2013). 
 We
 used the R software <em>GeoLigth</em>  for all steps in the
 analyses of light level data (Lisovski and Hahn 2012). To annotate twilight events we used the
 <em>twilightCalc</em>() function and a threshold value of 2
 lux. Each twilight event was visually inspected before proceeding.
 Positions were translated from light measurements into geographical
 positions using the <em>coord</em>() function. Because light
 intensity was only measured during a few short periods, appropriate
 calibrations were not possible. Consequently, we set sun elevation angles
 to -6° to -5.5° for all but three loggers, which were set to -4°.
 Adjustments were done by visually inspecting the discrepancy in latitude
 estimates between the two measurement periods in November and February and
 for the positions derived in May, when we know that the birds had returned
 to the breeding sites, adjustment were made to yield positions
 corresponding to south Sweden. This approach gives a somewhat less certain
 estimate of wintering positions compared to geolocators recording light
 continually, but we obtain reasonable estimates of wintering areas and
 major stopovers provided limited data. Total
 migration distances were estimated as the sum of great circle distances
 between well-defined consecutive stopovers and the breeding/wintering
 sites. To define stopovers we used light data during periods of residency
 as revealed by the MDLs (see above). Acceleration in the Z-axis was sampled every 5
 minutes with runs of 10 measurements at 5 time points, each separated by
 5s. Each measurement is a sample during 100 ms at 100 Hz in the range ±4g.
 For each run, the mean of the values was subtracted from each of the 10
 measurements to compensate for static gravity, and the recorded
 acceleration was considered as indicative of flight if at least 3 of the
 10 values were greater than <em>g</em>/3, where <em>g</em> is acceleration
 due to gravity. Each 5-minute sample was assigned the number of runs that
 indicate flight behavior, i.e. (0, .., 5). Every hour a summary of results
 from all 12 runs were stored according to the distribution of the samples
 across the different activity categories (0, .., 5). If the bird is
 perched and motionless the data stored will be (12, 0, 0, 0, 0, 0), and if
 it is flying with continuous wing beats the data are (0, 0, 0, 0, 0, 12).
 To illustrate flight activity in graphical actograms the hourly recordings
 are coded as ‘black’ to represent continuous flapping flight (0, 0, 0, 0,
 0, 12), and ‘white’ to represent no flight (12, 0, 0, 0, 0, 0), with
 shades of grey representing intermediate levels of activity (Hedenström et
 al. 2016; Bäckman et al. 2017). To identify flight periods based on the
 accelerometer data we derived weighted hourly activity scores by
 calculating the sum of each score multiplied by the number of events
 within each hour. Thus, the lowest score becomes 0 (0 * 12) and the
 highest possible score is 60 (5 *12). We then identified all hours with a
 weighted score &gt; 49, which we defined as an activity level
 corresponding to flapping flight. In almost all cases these hours were
 associated with sequences of high scores that could be identified as
 periods of flight. Start
 and end times of a flight
 were defined around the above defined flight period by subtracting all
 measurements (5 min periods) falling under a score of 3. Specifically, for
 a flight to be defined as ended zero-scores must be present during that
 hour. Thus, the starting and end points of flight periods, and hence
 flight duration, were calculated to a 5-minute resolution, provided that
 all scores &lt; 3 and all scores &gt;2 were recorded in sequence,
 respectively. Periods between well-defined flight periods were recorded as
 stopovers or winter/breeding site residency, depending on
 season. In some cases,
 particularly at the end of flight periods, hourly weighted scores were
 below 50. In such cases we examined the distribution of the raw activity
 scores (0-5) and defined the hour as <em>in flight</em> as
 long as no zero-scores were recorded. If a zero-score was recorded, we
 looked at the next hour to asses if the bird had landed by summing the
 number of zeros between the two hours. If that sum was &gt; 11
 (corresponding to more than or equal to 1 hour) the bird had landed, if it
 did not then the bird had continued, and we considered the full sequence
 as a continuous flight. However, when calculating the duration of the
 flight all 5 min scores &lt; 3 were omitted. References Bäckman,
 J., A. Andersson, T.  Alerstam, L. Pedersen, S. Sjöberg, K. Thorup,
 and A. Töttrup. 2017. Activity and migratory flights of individual
 free-flying songbirds throughout the annual cycle: method and first case
 study. Journal of Avian Biology 48:309-319. Hedenström, A., R. H. Klaassen, and S. Åkesson.
 2013. Migration of the
 little ringed plover <em>Charadrius dubius</em> breeding in
 south Sweden tracked by geolocators. Bird Study
 60:466-474. Hedenström, A., G. Norevik, K. Warfvinge, A. Andersson, J. Bäckman, and S. Åkesson. 2016. Annual 10-month aerial life-phase
 in the common swift <em>Apus apus</em>. Current
 Biology<strong> </strong>26:3066-3070. Lisovski, S. and S. Hahn. 2012. GeoLight -
 processing and analysing light-based geolocator data in R.  Methods in
 Ecology and Evolution 3:1055–1059.
description: # Data from: seasonal patterns and processes of migration in a
 long-distance migratory bird: energy or time minimization ## The
 Xnnn\_acc.csv files contain acceleration data for migrating little ringed
 plovers *Charadrius dubius* Description of the data and file structure The
 csv-files contain nine columns of data (A-I), where each data row is the
 summary of acceleration data sampled for one hour, consisting of 12
 samples. The data in each column are as follows: (A) Date and time. (B)
 Sequence number. Only of operational interest. The number is a control
 function that indicates that timing of the sampling routine works as
 expected. The number represents minutes passed since sampling was
 activated, truncated to 1 byte, i.e. when minutes is greater than 255, 256
 is subtracted. The first values are 60, 120, 180, 240, and then they are
 300-256 = 44, 104, 164, and so on. (C) acc (0), acc(1), .., acc(5),
 columns C-H: Every 5 minutes a summary is stored for how many of 5 samples
 indicated active flight, i-5- 0..5. Every hour the distribution of the 12
 runs are summarized according to activity level (acc(0), .., acc(5)). If
 the bird has been motionless during the preceding hour the data will be
 (12, 0, 0, 0, 0, 0), and if it has been flying continuously the numbers
 stored are (0, 0, 0, 0, 0, 12). (D) sumAcc (column I): The sum of columns
 C-H. The value should be 12 when sampling has followed the expected
 routine; if &lt;12 the logger may have failed to the record data during
 &gt;0 five minute periods. ## The Xnnn\_yyyy\_mm\_dd\_1.lux files contain
 light data for migrating little ringed plovers Charadrius dubius
 Description of the data and file structure The .lux files contain time
 stamped (UTC) light level measurements on a scale 1..255. In the loggers
 deployed in years 2016-2020 light level was measured for five day periods
 as follows (mmdd): 0715-0720, 0901-0906, 1115-1120, 0201-0206, 0415-0420,
 0505-0510, 0715-0720,.. The loggers deployed in 2021 had the following
 measurement schedule: 0615-0620, 0711-0720, 0901-0910, 1201-1210,
 0201-0210, 0301-0310, 0401-0410, 0501-0510, 0615-0620,.. ## The Little
 ringed plover reduced data.csv file contains reduced data used for
 statistical analyses for migrating little ringed plovers Charadrius dubius
 Description of the data and file structure The columns contain data as
 follows: A (Rnr) Ring number, Swedish Museum of Natural History B (Logger)
 Logger identification code C (Sex) Sex of bird, male (M) or female (F) D
 (Year) Year E (Season) Autumn (A) or spring (S) F (day start) Date and
 time of migratory flight start G (day stop) Date and time of migratory
 flight stop H (flight periods) Number of 5 min periods when flight
 activity score was 4 or 5 (possible score is 0..6) I (flight periods
 excluded) Number of 5 min periods when flight activity score was &lt;4
 (possible score is 0..6) J (flight duration min) Flight duration in
 minutes K (flight duration h) Flight duration in hours L (T2-T1) Time (in
 hours) between start and stop of migratory flight (difference between
 columns G and F; always equal or less than flight duration of column J
 depending on number of activity scores &lt;4).  M (cum flight dur)
 Cumulated flight duration for the migration season (hours) N (stopover
 duration) Time (hours) between consecutive migratory flights. For the
 first flight of a season the time required to accumulate fuel for the
 first flight was estimated by assuming a daily fuel deposition rate of
 1.3% of the lean body mass.  Q (MigDist) Season specific migration
 distance (km) Missing data code: null