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.

Material and methods
Satellite tracking data
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.
               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, n = 26 individuals) and morphological characteristics (following Schroeder et al. 2008, n = 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).
               We retrieved satellite tracking locations via the CLS tracking system (www.argos-system.org) and passed them through the “Best Hybrid-filter” algorithm (Douglas et al. 2012); this removed consecutive locations that exceeded a speed of 120 km h^-1 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).

Geolocator data
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.
               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).

Timing, routes, and orientation of juveniles and adults
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.

Mortality
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).
               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.

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.  

1.Juvenile timing with individual info_forR
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).

2.Adult and juvenile timing_forR
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).

3a.Juvenile_Autumn_Track
This file contains the points collected by the tracking devices carried by juveniles during autumn migration.

3b.Juvenile_Spring_Track
This file contains the points collected by the tracking devices carried by juveniles during spring migration.

3c.Adult_Autumn_Track
This file contains the points collected by the tracking devices carried by adults during autumn migration.

3d.Adult_Spring_Track
This file contains the points collected by the tracking devices carried by adults during spring migration.

4a.Juvenile_Longitudinal Intersection_Autumn
This file contains the longitude when crossing latitudinal boundaries (see Methods) of juveniles on autumn migration.

4b.Juvenile_Longitudinal Intersection_Spring
This file contains the longitude when crossing latitudinal boundaries (see Methods) of juveniles on spring migration.

4c.Adult and juvenile_Longitudinal Intersections
This file contains the longitude when crossing latitudinal boundaries (see Methods) for both adults and juveniles on southward and northward migration.

5a.Juvenile_Autumn_Longitudinal Displacement
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.

5b.Juvenile_Spring_Longitudinal Displacement
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.

5c.Adult and juvenile_Longitudinal Displacement_Autumn
This file contains the longitudinal displacement between latitudinal boundaries (see Methods) for both adults and juveniles on southward migration.

5d.Adult and juvenile_Longitudinal Displacement_Spring
This file contains the longitudinal displacement between latitudinal boundaries (see Methods) for both adults and juveniles on northward migration.

6a.Juvenile_nonstop
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.

6b.JuvenilevsAdult_nonstop
This file summarizes how many juveniles and how many adults that crossed the Sahara did so with a non-stop flight or not.

7a.Juvenile_sahara cross and mortality
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.

7b.JuvenilevsAdult_Sahara cross or not
This file summarizes how many juveniles and how many adults crossed the Sahara.

8a.JuvenilevsAdult_Mortality_Autumn
This file summarizes how many juveniles and how many adults died on southward migration.

8b.JuvenilevsAdult_Mortality_Spring
This file summarizes how many juveniles and how many adults died on northward migration.