DOI: 10.5061/dryad.02v6wwq4j
title: Age-dependent timing and routes demonstrate developmental plasticity in a long-distance migratory bird

description: 1. Longitudinal tracking studies have revealed consistent differences in
 the migration patterns of individuals from the same populations. The
 sources or processes causing this individual variation are largely
 unresolved. As a result, it is mostly unknown how much, how fast, and when
 animals can adjust their migrations to changing environments. 2. We
 studied the ontogeny of migration in a long-distance migratory shorebird,
 the black-tailed godwit (Limosa limosa limosa), a species known to exhibit
 marked individuality in the migratory routines of adults. By observing how
 and when these individual differences arise, we aimed to elucidate whether
 individual differences in migratory behaviour are inherited or emerge as a
 result of developmental plasticity. 3. We simultaneously tracked juvenile
 and adult godwits from the same breeding area on their south- and
 northward migrations. To determine how and when individual differences
 begin to arise, we related juvenile migration routes, timing, and
 mortality rates to hatch date and year of birth. Then, we compared adult
 and juvenile migration patterns to identify potential age-dependent
 differences. 4. In juveniles, the timing of their first southward
 departure was related to hatch date. However, their subsequent migration
 routes, orientation, destination, migratory duration, and likelihood of
 mortality were unrelated to the year or timing of migration, or their sex.
 Juveniles left the Netherlands after all tracked adults. They then flew
 non-stop to West Africa more often and incurred higher mortality rates
 than adults. Some juveniles also took routes and visited stopover sites
 far outside the well-documented adult migratory corridor. Such juveniles,
 however, were not more likely to die. 5. We found that juveniles exhibited
 different migratory patterns than adults, but no evidence that these
 behaviours are under natural selection. We thus eliminate the possibility
 that the individual differences observed among adult godwits are present
 at birth or during their first migration. This adds to the mounting
 evidence that animals possess the developmental plasticity to change their
 migration later in life in response to environmental conditions as those
 conditions are experienced.
description: <b>Material and
 methods</b><br> <b>Satellite tracking
 data</b><br> In both 2016 and 2017, we deployed 40
 solar-powered 5-g PTT-100s from Microwave Technology Inc. on juveniles,
 for a total deployment of 80 transmitters. All 80 transmitters were
 programmed to turn on for 8 hours and off for 24 hours. As a result of
 this duty cycle, we could only observe the timing of migration on a daily
 basis. We captured these juveniles by hand in the days just before they
 gained the ability to fly. Most juveniles were caught within our 12,000 ha
 study area in southwest Fryslân, The Netherlands (see Senner et al. 2015b
 for more details). However, in 2016 the number of fledged juveniles in our
 study area was considerably lower than average, so we also caught 4
 juveniles on the island of Ameland (53.45°N, 5.83°E; see Loonstra et al.
 2019a). To attach the transmitters, we used a leg-loop harness of 2-mm
 Dyneema rope. We also took ~30 μl of blood from the brachial vein for
 molecular sexing. We obtained
 migratory tracks from 28 of these juveniles (see Results): 24 from our
 study area and 4 from Ameland. Twenty-seven out of the 28 juveniles were
 molecularly sexed (12 males, 15 females); one analysis failed, so we sexed
 this bird based on its growth and morphological characteristics during
 five recaptures before fledging (Loonstra et al. 2018). Fifteen of the 28
 juveniles were marked with a code flag in the nest and their exact hatch
 dates were therefore known. The other 13 tracked juveniles were not
 captured in the nest, so we estimated their hatch dates using a
 sex-specific growth curve (Loonstra et al. 2018). This method yields an
 estimated hatch date that is accurate to within ±3 days, which is
 acceptable for our purposes given the large variation in hatch dates
 included in the study (range 2 May–13 June). The weight of the transmitter
 and the harness (~6 g) represented 3.2% ± 0.4 (range: 2.5–4.4%) of the
 total body mass at release, but this likely diminished to ~2% as the
 individuals continued to grow to adult size.<br>                To
 track the spatial distribution and mortality of adult godwits, we deployed
 32 solar-powered 9.5-g PTT-100s from Microwave Technology Inc. in 2015 and
 2016 (attachment ~10.5 g), and another four transmitters of 5 g in 2017.
 Thirty-four of these 36 transmitters were programmed to turn on for 8
 hours and turn off for 24 hours. One of the remaining two transmitters was
 programmed to turn on for 8 hours and off for 25 hours, and the other was
 programmed to turn on for 10 hours and off for 48 hours. We captured all
 36 adults on nests in the 220-ha Haanmeer polder, which lies in the centre
 of our larger study area. We captured adults using walk-in-traps,
 automated drop cages, or mist nets placed over the nest. We attached the
 leg-loop harnesses as we did for the juveniles. Based on a combination of
 molecular sexing (using a ~30 μl blood sample taken from the brachial vein
 at capture, <i>n</i> = 26 individuals) and morphological
 characteristics (following Schroeder et al. 2008, <i>n</i> =
 10 individuals), we determined that our sample of transmitter-carrying
 adults consisted of 34 females and 2 males. In 2015 and 2016, the loading
 factor of the transmitters was 3.4% ± 0.2 (range: 3.0–4.0%) of a female’s
 body mass at capture; in 2017, the loading factor was 1.9% for each of the
 two females and 2.2% for each of the two males (more details in Verhoeven
 et al. 2021).<br>                We retrieved satellite tracking
 locations via the CLS tracking system
 (www.argos-system.org) and passed them through the
 “Best Hybrid-filter” algorithm (Douglas <i>et al.</i> 2012);
 this removed consecutive locations that exceeded a speed of 120 km
 h<sup>-1</sup> while retaining location classes with qualities
 of 3, 2, 1, 0, A, and B. From this data we knew where individual godwits
 crossed nine arbitrary spatial boundaries that were spaced 4° of latitude
 apart across the godwit migration corridor. These boundaries ranged from
 52°N (the breeding grounds) to 20°N (just north of the southernmost
 African wintering grounds): 52˚N, 48˚N, 44˚N, 40˚N, 36˚N, 32˚N, 28˚N,
 24˚N, 20˚N. This allowed us to estimate for both south- and northward
 migration (1) an individual’s orientation, which we determined by
 calculating its longitudinal (i.e., east-west) movement, measured in
 kilometres, between the latitudinal boundaries; (2) the longitudinal
 distribution of tracks at each latitudinal boundary (see Fig. 2 in
 Verhoeven et al. 2021); and (3) where and when mortality occurred (see
 paragraph below for details). We calculated distances between points with
 the function “distHaversine” in the package “geosphere” (Hijmans
 2017).<br> <br> <b>Geolocator data</b><br>
 To track the timing of adult migration we used geolocators instead of
 satellite transmitters. We deployed 219 geolocators on 173 adult godwits
 from 2015 to 2018 in our study area. Geolocators were attached to a
 coloured flag that was placed on the adult’s tibia. The total weight of
 the attachment was ~3.7g, representing 1–1.5% of an individual’s body mass
 at capture. In subsequent years (2016–2019), we recaptured
 geolocator-carrying godwits to retrieve their geolocators and download the
 stored light-level data. We downloaded light-level data from 78
 geolocators retrieved from 64 adult godwits (24 males, 40 females). Twenty
 geolocators contained data for more than one season, although the second
 season was often incompletely logged because the battery stopped working.
 Thus, we obtained light-level data for a total of 98 complete and
 incomplete migrations.<br>                We used the package
 “FLightR” (Rakhimberdiev et al. 2017) to reconstruct the annual schedules
 of godwits from this light-level data. Detailed examples of this
 analytical routine using our own godwit data can be found in Rakhimberdiev
 et al. (2016, 2017). Briefly, using the FLightR function
 “find.times.distribution,” we estimated when individual godwits crossed
 the same nine spatial boundaries mentioned above. In these analyses, we
 excluded the crossing of the spatial boundary at 36°N (the Strait of
 Gibraltar) because we could not distinguish between birds stopping in
 northern Morocco and those stopping in southern Spain (see Verhoeven et
 al. 2019 for more details).<br> <br> <b>Timing, routes,
 and orientation of juveniles and adults</b><br> At each of the
 nine latitudinal boundaries, for both south- and northward migration, we
 used a general linear model with a Gaussian error distribution to examine
 the effects of hatching date, sex, and year on the (1) timing of juvenile
 migration; (2) longitude of juvenile migration routes; and (3)
 longitudinal movement of migrating juveniles between consecutive
 latitudinal boundaries. At each
 latitudinal boundary, for both south- and northward migration, we also
 compared the mean and variance of the (1) timing of crossing; (2)
 longitude of crossing; and (3) longitudinal movement between latitudinal
 boundaries of adults and juveniles tracked in the same years. Those years
 were 2016 and 2017 for southward migration and 2017-2019 for northward
 migration, because some individuals deferred northward migration (see
 Results).  To test for the equality of variances between adults and
 juveniles, we used a Levene’s test from the R-package “car”. If the
 variances were found to be equal, we used an ANOVA to test whether the
 mean was significantly different between adults and juveniles. If the
 variances were unequal, we compared the means with a Mann Whitney U test.
 We did not account for an individual’s sex in these analyses because we
 only tracked two adult males with satellite transmitters. However, we know
 from previous work that adult males and females do not differ in their
 migratory destinations (Hooijmeijer et al. 2013, Kentie et al. 2017,
 Senner et al. 2019, Verhoeven et al. 2019, 2021), which is further
 supported by more recent satellite-tracking efforts (2019-2021) that
 include more males (T. Piersma, R. Howison, J. Hooijmeijer, A.H.J.
 Loonstra and M.A. Verhoeven unpubl. data). We have also previously shown
 that the only difference in the migratory timing of adult males and
 females is that males leave the Netherlands on average 5 days earlier. The
 only likely consequence of a dataset with more males would therefore be an
 even bigger difference between adults and juveniles in their departure
 timing from the Netherlands than already observed (see Results). We
 therefore believe that our claims are robust and representative of godwit
 behaviour in general. We used a
 generalized linear model with a binomial error structure and a logistic
 link function to test whether the likelihood that juveniles (1) crossed
 the Sahara on their first southward migration and (2) did so by flying
 non-stop from the Netherlands was related to their departure date, year,
 or sex. We note that the dataset for the second analysis is a subset of
 the first dataset that only includes those individuals that crossed the
 Sahara. We also used a generalized linear model with a binomial error
 structure and a logistic link function to explore whether the adults and
 juveniles tracked in the same years on southward migration differed in the
 proportion of individuals that (1) crossed the Sahara and (2) did so with
 a non-stop flight from the Netherlands. <br>
 <b>Mortality</b><br> Where and when mortality occurred
 was assessed on the basis of data collected from our satellite
 transmitters. The adults outfitted with a 9.5-g transmitter were
 considered dead when their transmitter’s built-in activity sensor remained
 constant. The 5-g transmitters that four adults and all juveniles carried
 did not have such an activity sensor but did have a temperature sensor; we
 considered these birds dead when the measured temperature started to
 follow a day-night rhythm. These assumptions are also supported by the
 fact that we have never subsequently observed any of these adults to be
 alive during our extensive resighting efforts of marked birds (Verhoeven
 et al. 2018, Loonstra et al. 2019a).<br>                For this
 known-fate data, we used generalized linear models with a binomial error
 structure and a logistic link function to test whether (1) the likelihood
 that juveniles died on their first southward migration was related to
 their departure date, sex, or the year the juvenile hatched; and (2) the
 likelihood that juveniles died between departure from and return to the
 Netherlands was related to their hatch date, sex, or the year they
 hatched. We also made two figures to illustrate where (Fig. 3) and when
 (Fig. 4) mortality occurred during juvenile migration. We used the same
 type of generalized linear models to explore whether the adults and
 juveniles tracked in the same years differed in the proportion of
 individuals that died during south- and northward migration.
description: All files are numbered, a
 new number for each analysis/topic. Related files are given the same
 number but a different letter, i.e. files 3a, 3b, 3c, 3d are related to
 each other.   <b>1.Juvenile timing with
 individual info_forR</b><br> <i>This file includes the
 timing information of juvenile godwits on southward and northward
 migration. In case the juvenile did not make it back to The Netherlands
 there is no return date which is denoted by NA in column N.It also
 contains individual information, such as the sex of the individual
 (1=female, 2=male).</i><br> <br> <b>2.Adult and
 juvenile timing_forR</b><br> <i>This file contains the
 timing of every latitudinal crossing (see Methods) for both adults and
 juveniles (column C) on southward and northward migration. It also
 contains individual information, such as the sex of the individual
 (1=female, 2=male).</i><br> <br>
 <b>3a.Juvenile_Autumn_Track</b><br> <i>This file
 contains the points collected by the tracking devices carried by juveniles
 during autumn migration.</i><br> <br>
 <b>3b.Juvenile_Spring_Track</b><br> <i>This file
 contains the points collected by the tracking devices carried by juveniles
 during spring migration.</i><br> <br>
 <b>3c.Adult_Autumn_Track</b><br> <i>This file
 contains the points collected by the tracking devices carried by adults
 during autumn migration.</i><br> <br>
 <b>3d.Adult_Spring_Track</b><br> <i>This file
 contains the points collected by the tracking devices carried by adults
 during spring migration.</i><br> <br>
 <b>4a.Juvenile_Longitudinal Intersection_Autumn</b><br>
 <i>This file contains the longitude when crossing latitudinal
 boundaries (see Methods) of juveniles on autumn
 migration.</i><br> <br>
 <b>4b.Juvenile_Longitudinal Intersection_Spring</b><br>
 <i>This file contains the longitude when crossing latitudinal
 boundaries (see Methods) of juveniles on spring
 migration.</i><br> <br> <b>4c.Adult and
 juvenile_Longitudinal Intersections</b><br> <i>This file
 contains the longitude when crossing latitudinal boundaries (see Methods)
 for both adults and juveniles on southward and northward migration.
 </i><br> <br> <b>5a.Juvenile_Autumn_Longitudinal
 Displacement</b><br> <i>This file contains the longitude
 at subsequent latitudinal boundaries of juveniles on autumn migration,
 which is necessary to calculate the longitudinal displacement between
 latitudinal boundaries.</i><br> <br>
 <b>5b.Juvenile_Spring_Longitudinal Displacement</b><br>
 <i>This file contains the longitude at subsequent latitudinal
 boundaries of juveniles on spring migration, which is necessary to
 calculate the longitudinal displacement between latitudinal
 boundaries.</i><br> <br> <b>5c.Adult and
 juvenile_Longitudinal Displacement_Autumn</b><br>
 <i>This file contains the longitudinal displacement between
 latitudinal boundaries (see Methods) for both adults and juveniles on
 southward migration. </i><br> <br> <b>5d.Adult and
 juvenile_Longitudinal Displacement_Spring</b><br>
 <i>This file contains the longitudinal displacement between
 latitudinal boundaries (see Methods) for both adults and juveniles on
 northward migration. </i><br> <br>
 <b>6a.Juvenile_nonstop</b><br> <i>This file
 contains all juveniles that crossed the Sahara and has information on
 whether they flew their non-stop or not. It also contains a column that
 specifies whether the juvenile died on southward migration and whether it
 returned to The Netherlands or not.</i><br> <br>
 <b>6b.JuvenilevsAdult_nonstop</b><br> This file
 summarizes how many juveniles and how many adults that crossed the Sahara
 did so with a non-stop flight or not.<br> <br>
 <b>7a.Juvenile_sahara cross and mortality</b><br>
 <i>This file contains all juveniles and has information on whether
 they crossed the Sahara or not. It also contains a column that specifies
 whether the juvenile died on southward migration and whether it returned
 to The Netherlands or not.</i><br> <br>
 <b>7b.JuvenilevsAdult_Sahara cross or not</b><br> This
 file summarizes how many juveniles and how many adults crossed the
 Sahara.<br> <br>
 <b>8a.JuvenilevsAdult_Mortality_Autumn</b><br> This file
 summarizes how many juveniles and how many adults died on southward
 migration.<br> <br>
 <b>8b.JuvenilevsAdult_Mortality_Spring</b><br> This file
 summarizes how many juveniles and how many adults died on northward
 migration.