1
Appendix S1
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Trapping procedure
3
Storks often forage close to tractors that are mowing agricultural fields. We used this
4
phenomenon to trap adult storks by shooting nets (Super Talon, Zhuhai ZONSO Electronics)
5
from a mowing tractor. The net gun was positioned on the side of the tractors and was triggered
6
using a string from inside the tractor’s cabin. No birds were injured during these trapping efforts.
7
Of the 62 adults that were trapped, 51 were local breeders, 5 were non-breeding storks and 6
8
could not be located. Juveniles were trapped in their nests approximately one week prior to
9
fledging. We fitted transmitters to all siblings in the nest (1-4, average 1.9) and when possible
10
preferred nests of tagged adults. This resulted in tagging 64 juveniles from 34 nests. In 12 of
11
these nests also one parent carried a transmitter, and in 3 both parents did. Data does not include
12
juveniles of the same nest from different years.
13
Data retrieval
14
Storks had to be approached to approximately 300 m in order to download the data, stored
15
onboard the transmitter via a UHF radio link. Birds were located using a built-in radio signal
16
(pinger) of the transmitter, which enabled detection from a maximal distance of approximately 5-
17
20 km. For adults, we were able to download data from 80% of the individuals in the following
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year upon return from the migration to their breeding area. The juveniles' transmitters also sent
19
SMS (see below) with locations that enabled us to follow the juvenile storks and to download
20
data from the birds during migration from Germany to Turkey (in fall 2013) and while passing
21
through Israel (in 2012 and 2013) via the UHF radio link. This mostly required to approach the
22
roosting flock at night, which was dependent on cellular reception and accessibility of the site,
23
thus it was feasible for ca. 75% of the storks. The SMS transmission also provided the
24
opportunity to detect juvenile mortality events from consistently repeated locations and to
25
retrieve transmitters from dead juveniles, some in very remote locations in Africa, providing the
26
entire data stored onboard the transmitter and, in some cases, also an assessment of the cause of
27
death.
28
Different transmitter types for adults and juveniles
29
With the exception of one unit, we used different transmitter types for adults and juveniles (both
30
manufactured by E-obs); the adult tags, at nearly half costs of juvenile tags, had only UHF data
31
download option (see above) whereas the juvenile tags had an additional real-time data
32
transmission by SMS via a GSM unit. The reason for this lies in the high nest fidelity of adults
33
versus low fidelity and high mortality of first-year juveniles which made data retrieval of UHF
34
tags possible only for adults (see above), as well as in the higher cost and lower availability of
35
the GSM tags. The GSM tags carried by the juveniles weighted 11 gram more than adult tags
36
(equivalent to 0.2% of body weight). Additionally, they were 0.5 cm taller, with two additional
37
antennas of 2.6 X 0.15 cm (length X width) (Fig S3) which increased tag frontal area by 2.2
38
squared cm. For reference, stork wing span is about 2.16 m, thus this increase is about 1.2% of
39
stork frontal area (calculated using “Flight 1.24” software by C. Pennyquick). We believe that
40
these weight and shape differences did not account for the age-related differences in flight
41
behavior described in the manuscript for the following reasons: a single adult was equipped with
42
a GSM tag and its flight ODBA was still low, the second lowest among the adults. The
43
probability of getting this adults’ ODBA value or lower in the juveniles’ flight ODBA
44
distribution was significantly low (t = -2.68, DF = 18, P(x≤t) = 0.015), suggesting that age rather
45
than transmitter type accounted for this difference. In addition, according to bird flight modeling
46
(Pennycuick 2008), the extra weight and frontal area of the juveniles’ tags reduced gliding
47
speeds and increased flapping power demands only by 0.6% (“Flight 1.24” software by C.
48
Pennyquick). Previous studies that added or elongated antennas found no significant effects on
49
flight performance (Pennycuick et al. 2012; Zenzal, Diehl & Moore 2014) and in these studies,
50
the relative size of the antennas, compared to the size of the tagged passerines was much bigger
51
than in our study. Finally, the flight improvement of juveniles, resembling adults’ flight
52
performance by the end of migration (see Results) also implies that no significant effect of the
53
tag differences on the juveniles is evident.
54
Behavioral classification of ACC records
55
A supervised machine learning algorithm was trained on 3,815 ground-truthed ACC bouts for
56
which behavior was determined by field observations on free-ranging tagged birds in Germany.
57
The focal tagged bird was located and identified by a unique frequency of a pinger radio signal
58
emitted from its transmitter, by spotting the transmitter on the bird and in some cases also by
59
reading the ring number. Exact matching of the ACC record and the observation was possible
60
through a dedicated signal of the pinger indicating the start of the 3.8 sec ACC measurement
61
bout. Forty two summary statistics computed for each ACC bout (see Nathan et al. 2012) were
62
used to train a radial-basis-function kernel support vector machine (RBF-SVM) for classifying
63
seven behavioral categories: Active flight (flapping), passive flight (soaring or gliding), walking,
64
standing and sitting. The walking category was further subdivided into walking and pecking, and
65
the standing category into standing and preening, both using additional RBF-SVM classifiers.
66
We tested the accuracy of our machine learning classifier in classifying ground-truth behaviors
67
by using 9/10 of the observations to train the classifier and the remaining 1/10 to test its
68
accuracy. We repeated this procedure 10 times and the averages scores are presented in the
69
confusion matrices in tables S3-5. In the final stage, the classifiers were used to classify
70
behaviors of all the non-verified ACC records. The classification was applied using the scikit
71
toolbox in Python (Pedregosa et al. 2011).
72
Incorporating atmospheric data
73
For each GPS point, zonal and meridional wind components at the pressure level most
74
compatible for the recorded height above the ground, and thermal uplift velocity (see Bohrer et
75
al. 2012) were bi-linearly interpolated through the Env-DATA track annotation tool of
76
MoveBank (Dodge et al. 2013) based on weather data from the European Centre for Medium-
77
Range Weather Forecasts (ECMWF) Global Atmospheric Reanalysis (temporal resolution of 6
78
hours and spatial resolution of 0.7°). Height above ground, which was used for the wind
79
interpolation, was calculated using elevation data from the ASTER ASTGTM2 30 m Digital
80
Elevation Map (DEM; 30 m resolution).
81
Extracting mutually exclusive juvenile-adults pairs that migrated together
82
Our dataset included 58 juvenile-adult pairs that migrated together originating from 25 juveniles
83
and 16 adults. In order to generate an independent dataset, in which each individual is part of one
84
pair only, we extracted from those 58 pairs 16 mutually exclusive pairs using an automatic
85
algorithm. The algorithm was based on selecting the pairs with the longest joint migration time
86
first, for maximizing the number of data points for the analysis. The algorithm was applied
87
entirely by computer software and included the following steps: a) pairs were sorted in a list
88
according to their joint migration time, b) the pair with the longest time was chosen and all other
89
pairs comprised of one of the chosen pair’s individuals were removed from the list, c) “b” was
90
repeated until pairs list was empty. d) This yielded 16 mutually exclusive juvenile-adult pairs
91
and left out 9 juveniles. For each of these juveniles the pair with the longest joint time was
92
chosen and added to the above mutually exclusive set of pairs resulting in a set of 25 joint
93
migration pairs where some adults were present in more than one pair but each juvenile was
94
paired to only one adult. e) The final step was to average the joint migration parameters for all
95
the adults that were paired to more than one juvenile, coming back to a set of 16 mutually
96
exclusive joint migration records, where each record consists of joint migration parameters of a
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pair of one adult and one juvenile or of an average of a few pairs of the same adult with several
98
juveniles (2-4).
99
Data cutoff for comparing ‘successful’ versus ‘unsuccessful’ juveniles
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The comparison of ‘successful’ versus ‘unsuccessful’ juveniles was based on high-resolution
101
data of only the first third of the fall migration, until crossing 40°N (Turkey), as opposed to all
102
other analyses that included data until the birds crossed 17.5°N. The reason for this is twofold:
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First, the requirement for a comparison over the same migration segment. It is invalid to compare
104
different migration tracks, e.g. of a bird that flew from Germany to Turkey and died there and a
105
bird that completed its migration and reached Sudan, because of the different environmental
106
conditions encountered in each of these tracks. Second, the 40°N cutoff maximized the amount
107
of data for this analysis: comparing a larger migration segment, like only those birds that
108
accomplished the migration (17.5° N) would have resulted in a very small sample size for the
109
‘unsuccessful’ juveniles (n = 3), and on the other hand, comparing a shorter migration segment
110
reduced the data size. This was also related to the fact that storks were followed for data
111
download in Europe until Turkey, and hence the number of available high-resolution tracks
112
substantially decreased afterwards.
113
Flight ODBA repeatability
114
We conducted a repeatability analysis in order to examine whether flight efficiency was a
115
consistent attribute of individuals. We split the data into odd and even flight days and tested the
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repeatability (Rpt) of individuals’ flight ODBA between these datasets using a linear mixed
117
model following the methods described in (Nakagawa & Schielzeth 2010), calculating
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significance with likelihood ratio test and CI with bootstrapping. The model included fixed
119
factors (thermal uplift, tail and cross wind), and individual as a random factor. Both adults (Rpt =
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0.63, CI95%: 0.4-0.79, p < 0.001) and juveniles (Rpt = 0.68, CI95%: 0.33-0.87, p = 0.004) showed
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significant repeatability in flight ODBA which did not differ from each other.
122
123
References
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125
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127
128
129
130
131
132
133
134
135
136
137
138
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140
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Bohrer, G., Brandes, D., Mandel, J.T., Bildstein, K.L., Miller, T.A., Lanzone, M., Katzner, T., Maisonneuve,
C. & Tremblay, J.A. (2012) Estimating updraft velocity components over large spatial scales:
contrasting migration strategies of golden eagles and turkey vultures. Ecology Letters, 15, 96103.
Dodge, S., Bohrer, G., Weinzierl, R., Davidson, S., Kays, R., Douglas, D., Cruz, S., Han, J., Brandes, D. &
Wikelski, M. (2013) The environmental-data automated track annotation (Env-DATA) system:
linking animal tracks with environmental data. Movement Ecology, 1, 3.
Nakagawa, S. & Schielzeth, H. (2010) Repeatability for Gaussian and non-Gaussian data: a practical guide
for biologists. Biological Reviews, 85, 935-956.
Nathan, R., Spiegel, O., Fortmann-Roe, S., Harel, R., Wikelski, M. & Getz, W.M. (2012) Using tri-axial
acceleration data to identify behavioral modes of free-ranging animals: general concepts and
tools illustrated for griffon vultures. Journal of Experimental Biology, 215, 986-996.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer,
P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. &
Duchesnay, E. (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning
Research, 12, 2825-2830.
Pennycuick, C.J. (2008) Modelling the Flying Bird. Elsevier Academic Press Inc, San Diego.
Pennycuick, C.J., Fast, P.L.F., Ballerstadt, N. & Rattenborg, N. (2012) The effect of an external transmitter
on the drag coefficient of a bird's body, and hence on migration range, and energy reserves after
migration. Journal of Ornithology, 153, 633-644.
Zenzal, T.J., Diehl, R.H. & Moore, F.R. (2014) The impact of radio-tags on Ruby-throated Hummingbirds
(Archilochus colubris). Condor, 116, 518-526.
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147
Table S1: summary of tagged individuals. For adults the table summarizes the period of one year
148
after tagging (when data download could take place), and for juveniles the period until October,
149
after fall migration ends.
150
151
152
153
Counts
Adults
Juveniles
total
62
64
completed east migration – took part in the analysis
40
42
migrated west or did not migrate
6
6
could not be located after tagging1
5
0
dead prior to or during migration
2
15
missing2
8
1
tag malfunction
1
0
1
Adults in this category were probably non-local storks as they could not be located in the
following days after tagging.
2
missing adults could not be located in the year after tagging, missing juveniles stopped sending
SMS’s
154
Table S2: number of tracks and individuals with high-resolution data of the whole migration per
155
year. Each juvenile (n = 19) contributed one track, whereas 15 of the adult individuals (n = 40)
156
contributed 2 tracks (1 in 2011-2, 13 in 2012-3, and 1 in 2011 & 2013), and 3 individuals had 3
157
tracks.
158
2011
2012
2013
number of juveniles
0
6
13
number of tracks of adults
8
24
29
159
Table S3: Summary of comparisons of adult (n = 40) versus juvenile (n = 19) behavioral
160
parameters during flight days in the fall migration. Parameters average per migration journey (n
161
= 80) were compared using GLMM (gamma distribution, fixed factor: age, random factors:
162
individual, year and family ID, df = 1,78).
Parameter
163
164
Adults
Juveniles
F (1,78)
P value
(mean ± SE)
(mean ± SE)
flight ODBA (m/s2)
2.28 ± 0.03
2.60 ± 0.06
24.75
<0.001
ground ODBA (m/s2)
0.99 ± 0.02
0.99 ± 0.03
0
0.99
flapping ratio
0.17 ± 0.006
0.21 ± 0.013
8.63
0.004
relative foraging time
0.25 ± 0.01
0.26 ± 0.01
1.6
0.28
pecking ratio
0.31 ± 0.02
0.37 ± 0.02
6.64
0.012
relative preening time
0.22 ± 0.02
0.16 ± 0.01
4.17
0.044
165
Table S4. Confusion matrix for classifying the general behaviors (stage 1 of the classifier). Each
166
cell displays detection percentages, for example, 89.46% of the active flight observations were
167
identified correctly whereas 1.25% of them were classified as passive flight.
168
Classifier output (%)
active flight
passive flight walking standing sitting
active flight
89.46
1.25
3.93
5.36
0.00
passive flight
2.11
88.33
4.22
4.33
1.00
Ground
walking
0.20
0.13
93.59
5.81
0.27
truth
standing
0.22
0.00
4.47
94.19
1.13
sitting
0.00
0.00
0.34
9.38
90.27
Table S5. Confusion matrix for classifying between walking & pecking (stage 2 of the classifier).
169
Classifier output (%)
pecking
walking
pecking
64.81
35.19
Ground
truth
walking
4.69
95.31
Table S6. Confusion matrix for classifying between standing & preening (stage 3 of the
170
classifier).
Ground
truth
171
preening
standing
Classifier output (%)
preening
standing
75.79
24.21
8.49
91.51
y = -0.000368x + 3.33
R2= 0.7, P<0.001
172
173
Fig. S1. Changes in flight ODBA throughout the migration. An example of a regression line,
174
generated for one juvenile, of daily flight ODBA in relation to accumulated migration distance
175
(from breeding ground). Each data point represents a daily average of flight ODBA.
176
y = 0.00015x + 1.18
R2= 0.46, P<0.001
177
178
179
Fig. S2. Changes throughout the migration in thermal uplift velocity. A positive relationship
180
between travel distance and the thermal uplift velocity (daily average): as migration progresses
181
south, thermals become stronger.
182
183
184
185
186
Fig. S3. Different transmitter types: on the left: GSM tag used for juveniles, on the right: UHF
187
tag used for adults. GSM tag is 0.5 cm taller with 2 additional antennas. The middle antenna size
188
was equal for both tag types, even though in the picture it is not (because the UHF tag antenna
189
was broken and re-attached).
190