1
APPENDIX S1
2
Figure S1. Examples of glide polars, Vbg (best glide speed) and Vopt (the optimal
3
“speed-to-fly” speed). The glide polars of White stork (WS), Lesser spotted eagle (LSE),
4
Booted eagle (BE) and European bee-eater (EBE). Blue close circles represent species
5
Vbg. Close and open red circles represent species Vopt when climbing rate while soaring is
6
1 m s-1 (Vc1, close black circle) or 2 m s-1 (Vc2, open black circle), respectively.
7
8
Figure S2. The four nested grids of the Regional Atmospheric Modeling System
9
(RAMS) application used for the tracking radar’s locality near Idan in the northern
10
Arava, Israel. The black squares in panels A, B, and C represent the nested grid
11
domains. (A) 992 km × 992 km grid, with grid element size of 16 km × 16 km. (B) 248
12
km × 248 km grid, with grid element size of 4 km × 4 km. (C) 62 km × 62 km grid, with
13
grid element size of 1 km × 1 km. (D) 19.5 km × 19.5 km grid, with grid element size of
14
250 m × 250 m.
15
16
Figure S3. Convergence of gliding airspeed and intra-specific morphological
17
variation. Gliding airspeed (Va) converged to a narrow range of 2.7 ms-1 (white
18
background) within the much larger theorized range spanning 13.7 ms-1. For each species,
19
the mean Va (actual gliding airspeed) is depicted by a black dot within a bar that indicates
20
the range between Vbg (best glide speed) and Vopt (the optimal “speed-to-fly” speed); left
21
and right tips, respectively). Species abbreviations are given at the right of the figure (see
22
Table 1). This figure is identical to Figure 3 of the main text, with the addition of mean
23
Va calculated by bootstrapping the Risk Aversion Flight Index (RAFI; red open circles),
24
incorporating realistic estimates of intraspecific variation of bird biometric attributes
25
(Table S8). The bootstrap estimates also converged to a very similar small zone (marked
26
by dashed red lines), illustrating the robustness of this result to variation among
27
individuals.
28
29
Figure S4. The relationships between four species-specific morphological variables
30
and the Risk Aversion Flight Index (RAFI). The lines represent linear best fit of pairs
31
of variables. Species abbreviations are given following Table 1.
32
33
34
Table S1. Standard deviation (STD) of biometric attributes implemented in
35
bootstrapped estimation of the Risk Aversion Flight Index (RAFI), compared to
36
RAFI estimated from the mean value of each parameter.
Species
Body mass Wing span Wing area Mean bootstrapped
Mean RAFI
STD
STD
STD
RAFI
(from Table
(kg)
(m)
(m2)
(2.5%-97.5%)
1)
Ciconia nigra
0.510
0.130
0.070
0.992 (0.958-1.014)
0.999
Ciconia ciconia
0.612
0.151
0.091
0.940 (0.924-0.958)
0.946
Pernis apivorus
0.136
0.089
0.036
0.285 (0.265-0.302)
0.290
Milvus migrans*
0.121
0.044
0.022
0.420 (0.401-0.439)
0.429
Circus aeruginosus
0.111
0.093
0.032
0.447 (0.433-0.458)
0.450
Circus pygargus
0.051
0.079
0.020
0.002 (-0.022-0.019)
0.004
Accipiter brevipes
0.037
0.049
0.010
0.243 (0.221-0.268)
0.258
Buteo buteo vulpinus*
0.069
0.047
0.022
0.101 (0.090-0.118)
0.109
Aquila pomarina
0.343
0.126
0.072
0.395 (0.378-0.407)
0.397
Aquila nipalensis*
0.467
0.144
0.030
0.812 (0.790-0.838)
0.820
Hieraaetus pennatus
0.101
0.081
0.028
0.377 (0.355-0.411)
0.406
Merops apiaster**
0.004
0.010
0.002
0.049 (0.022-0.062)
0.049
37
Notes: Results of 1000 runs randomly assigning value for each biometric parameter from
38
a normal distribution with the mean from Table 1 and standard deviation from this Table.
39
For species marked with an asterisk (*), STD was estimated based on species-specific
40
data in Mendelsohn et al. (1989). For Bee-eaters (**), STD was estimated from data in
41
Table S10. For all other species, STD was calculated from the mean using the following
42
conservative (90th percentile) estimates of the coefficient of variation based on data from
43
38 diurnal raptor species for body mass and wing area and 31 species for wing span
44
(Mendelsohn et al. 1989): 17% for body mass, 14% for wing area, and 7% for wing span.
45
46
Table S2. Risk Aversion Flight Index (RAFI) explained by species-specific
47
morphological traits using phylogenetically controlled regression.
Morphological
trait
Linear model
Logarithmic model
best fit equation
AICc
best fit equation
AICc
Body mass
y = 0.08 + 0.27x
382.95
y= 0.45 + 0.22log(x)
385.35
Wing span
y= −0.44 + 0.61x
386.18
y= 0.24 + 0.57log(x)
388.25
Wing area
y= − 0.09 + 1.70x
393.81
y= 0.78 + 0.27log(x)
390.98
Wing loading
y= −0.45 + 0.24x
369.01
y= −0.71 + 0.93log(x)
352.77
Aspect ratio
-
-
-
-
48
Notes: Results of regressions between species-specific morphological traits (independent
49
factor; averaged for each species, see Table 1) and RAFI (dependent factor; averaged for
50
each species) using linear (y = a x + b) and logarithmic (y = a log x + b) models. The
51
regressions were computed with the phylogenetically controlled generalized least squares
52
method developed by Garland and Ives (2000) to minimize the effects of phylogenetic
53
bias. Phylogenetic data were taken from Griffiths et al. (2007) and Hackett et al. (2008).
54
The best model was selected based on the lowest Akaike Information Criterion value,
55
modified for small sample sizes (AICc; Burnham & Anderson 2002). Phylogenetic
56
regressions with aspect ratio as the independent variable resulted in negative R2 values
57
for both linear and logarithmic models, indicating that the model is unsuitable in these
58
cases.
59
Table S3. Risk Aversion Flight Index (RAFI) explained by species-specific
60
morphological traits.
Morphological
trait
Linear model
Logarithmic model
best fit equation
AICc
best fit equation
AICc
Body mass
y = 0.11 + 0.25x
−39.94
y= 0.49 + 0.23log(x)
−33.10
Wing span
y = −0.32 + 0.54x
−32.54
y= 0.29 + 0.57log(x)
−28.33
Wing area
y = 1.45x
−33.44
y= 0.84 + 0.27log(x)
−28.01
Wing loading
y = −0.37 + 0.22x
−45.66
y= −0.62 + 0.86log(x)
−46.36
Aspect ratio
y = 0.70 − 0.04x
−18.50
y= 0.89 − 0.23log(x)
−18.47
61
Notes: The results of simple, phylogenetically uncorrected, regression between species-
62
specific morphological traits (independent factor; averaged for each species, see Table 1)
63
and RAFI (dependent factor; averaged for each species). This analysis is similar to that
64
reported in Table S1, but was conducted using simple linear regressions without
65
controlling for phylogenetic effects.
66
67
Table S4. Circling radius in thermals explained by species-specific morphological
68
traits.
Morphological
trait
Linear model
Logarithmic model
best fit equation
AICc
best fit equation
AICc
Body mass
y = 10.02 + 0.72x
−5.73
y = 11.16 + 0.72log(x)
−4.70
Wing span
y = 8.64 + 1.69x
−3.94
y= 10.51 + 1.88log(x)
−2.16
Wing area
y= 9.68 + 4.29x
−2.56
y= 12.30 + 0.90log(x)
−1.50
Wing loading
y= 8.60 + 0.65x
−8.30
y= 7.99 + 2.43log(x)
−5.93
Aspect ratio
y= 11.20 − 0.03x
9.26
y = 11.57 − 0.31log(x)
9.26
69
Notes: The results of simple regressions between species-specific morphological traits
70
(independent factor; averaged for each species, see Table 1) and circling radius in
71
thermals (dependent factor; averaged for each species).
72
73
Table S5. Pearson’s r of the correlation between the five morphological traits
74
examined in radar tracked soaring migrants.
75
76
Body mass Wingspan Wing area Wing loading Aspect ratio
Mass
1.00
0.92
0.96
0.96
-0.05
Wingspan
-
1.00
0.97
0.86
-0.06
Wing area
-
-
1.00
0.88
-0.17
Wing loading -
-
-
1.00
-0.06
-
-
-
-
1.00
Aspect ratio
77
Notes: The morphological attributes were given in Bruderer et al. (2010), except for the
78
steppe eagle, provided in Bruderer & Boldt (2001).
79
80
Table S6. Intra-specific variation in climb rate explained by Turbulence Kinetic
81
Energy (TKE).
Source
DF
MS
F
P
Species
11
0.35
0.75
0.69
TKE
1
9.33
9.33
<0.001
Species * TKE
11
0.43
0.43
0.51
Error
1322
0.47
82
Notes: The results of ANCOVA examining whether intra-specific variation in climb rate
83
during circling is affected by TKE (Turbulence kinetic energy). TKE (independent
84
covariate), and species (independent categorical variable) are explanatory variables in a
85
model that includes climb rates calculated from tracks of individual birds (dependent
86
variable).
87
88
Table S7. Intra-specific variation in Risk Aversion Flight Index (RAFI) explained
89
by soaring conditions.
Source
DF
MS
F
P
Species
11
7.81
21.01
<0.001
TKE
1
28.04
75.41
<0.001
Species * TKE 11
0.41
1.09
0.36
Error
0.37
1322
90
Notes: The results of ANCOVA examining whether intra-specific variation in RAFI is
91
affected by soaring conditions. Turbulence kinetic energy (TKE, independent covariate),
92
and species (independent categorical variable) are explanatory variables in a model that
93
includes RAFI values calculated from tracks of individual birds (dependent variable).
94
High values of TKE indicate good soaring conditions.
95
96
Table S8. Intra-specific variation in Risk Aversion Flight Index (RAFI) explained by
97
bird altitude at the start of the gliding phase.
Source
DF
MS
F
Species
11
8.85
24.24 <0.001
Gliding altitude
1
35.44 97.03 <0.001
Species * Gliding altitude 11
Error
0.52
1.41
P
0.16
1322 0.37
98
Notes: The results of ANCOVA examining whether intra-specific variation in RAFI is
99
affected by bird altitude at the start of the gliding phase. Initial gliding height
100
(independent covariate), and species (independent categorical variable) are explanatory
101
variables in a model that includes RAFI values calculated from tracks of individual birds
102
(dependent variable).
103
104
Table S9. The effect of migration season on Risk Aversion Flight Index (RAFI) and
105
gliding airspeed (Va).
RAFI
Va
Source
DF
MS
F
P
DF
MS
F
P
Species
11
8.59
21.85
<0.001
11
36.93
2.83
<0.001
Season
1
0.48
1.23
0.27
1
24.44
1.87
0.17
Error
1335
0.39
1335
13.06
106
Notes: The results of ANOVA examining whether RAFI and gliding airspeed vary
107
between spring and autumn. Migration season and species are explanatory variables in a
108
model that includes RAFI values calculated from tracks of individual birds (dependent
109
variable).
110
111
Table S10. European bee-eaters (Meops apiaster) biometric data.
Bird ID
112
113
Body mass
Wing span
(ring number)
(kg)
(m)
CC29987
0.0544
0.442
CC30702
0.0488
0.442
CC30813
0.0643
0.441
CC30837
0.0552
0.415
CC30881
0.0526
0.441
CC30885
0.0535
0.442
CC30896
0.0512
0.443
C48417
0.0573
0.444
C48912
0.0531
0.439
CC30955
0.0543
0.449
CC30969
0.0542
Notes: Data were collected in Southern Israel at the springs of 2005 and 2006.
114
APPENDIX S1 REFERENCES
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
1.
Bruderer B. & Boldt A. (2001). Flight characteristics of birds: I. radar measurements of speeds.
Ibis, 143, 178-204.
144
145
2.
Bruderer B., Peter D., Boldt A. & Liechti F. (2010). Wing‐beat characteristics of birds recorded
with tracking radar and cine camera. Ibis, 152, 272-291.
3.
Burnham K.P. & Anderson D.R. (2002). Model selection and multi-model inference: a practical
information-theoretic approach. Springer.
4.
Garland T. & Ives A.R. (2000). Using the past to predict the present: confidence intervals for
regression equations in phylogenetic comparative methods. Am Nat, 155, 346-364.
5.
Griffiths C.S., Barrowclough G.F., Groth J.G. & Mertz L.A. (2007). Phylogeny, diversity, and
classification of the Accipitridae based on DNA sequences of the RAG‐1 exon. J Avian
Biol, 38, 587-602.
6.
Hackett S.J., Kimball R.T., Reddy S., Bowie R.C.K., Braun E.L., Braun M.J., Chojnowski J.L.,
Cox W.A., Han K.L. & Harshman J. (2008). A phylogenomic study of birds reveals their
evolutionary history. Science, 320, 1763-1768.
7.
Mendelsohn J.M., Kemp A.C., Biggs H.C., Biggs R. & Brown C.J. (1989). Wing areas, wing
loadings and wing spans of 66 species of African raptors. Ostrich, 60, 35-42.