Appendix S1. Details of the breakpoint procedure
We estimated the departure date as the first breakpoint observed on this regression line for each
bird using Muggeo (2003; 2009)’s breakpoints estimates from segmented linear regression. This
procedure attempts to estimate departure date (i.e. breakpoint) by fitting iteratively a model with a
linear predictor (that contains the breakpoint estimation terms, see Muggeo 2003) until algorithm
convergence. For each individual, we ran three different procedures by fixing the breakpoint
estimation term at 0 to 30 days, 30 to 60 days or 60 to 90 days (Eraud et al. 2011). Although this
method was chosen to estimate a shift in the distance patterns, its limitation had to be first
discussed. For instance we were unable to detect departure date for an individual with linear
movements (proportional distances between successive distances) along the 90 days. Moreover,
individuals can move away from the release site before a shift was observed. Both situations
suggest that an individual can move away from the release site either 1) without detecting a
breakpoint or 2) before a breakpoint was observed. Regarding this inherent limitation, we decided
to separately analyse departure date and net dispersal distances.
References
Eraud, C., Jacquet, A. & Legagneux, P. (2011) Postfledging movements, home range and survival
of juvenile Eurasian collared-doves. Condor, 113, 150-158.
Muggeo, V.M.R. (2003) Estimating regression models with unknown break-points. Statistics in
Medicine, 22, 3055–3071.
Muggeo, V.M.R. (2009) Segmented: Segmented relationships in regression models. R package
version 0.2-6.
Table S1. Details of the dispersal analysis sample (N = 436 individuals) of captive-bred houbara bustards with body weight, release
age by years and by sex of the equipped birds. The mean release group size by years is also given.
Male
Female
Year
N
Age (days)
Body Weight (g)
N
Age (days)
Body Weight (g) Release group size
2001
15
431 ± 98
1726 ± 101
14
351 ± 89
1160 ± 120
10 ± 3
2002
24
220 ± 97
1604 ± 170
24
254 ± 83
1124 ± 103
23 ± 9
2003
14
280 ± 120
1598 ± 161
34
242 ± 54
1122 ± 116
35 ± 11
2004
33
134 ± 11
1295 ± 164
14
126 ± 24
962 ± 120
86 ± 54
2005
19
207 ± 82
1376 ± 299
20
246 ± 80
1079 ± 132
40 ± 13
2006
47
241 ± 539
1586 ± 156
49
250 ± 52
1131 ± 116
72 ± 27
2007
9
191 ± 5
1564 ± 172
17
201 ± 30
1191 ± 75
134 ± 35
2008
31
217 ± 63
1454 ± 165
33
241 ± 66
1068 ± 151
221 ± 111
2009
17
196 ± 99
1200 ± 246
22
238 ± 90
937 ± 141
292 ± 101
Figure S1. Locations of the weather stations in Morocco. Houbara bustards were bred in two
breeding stations, Missour and Enjil (respectively grey and white circles).
Appendix S2. Model selection procedure of the linear mixed effect models
For all models, we started from the global model (all explanatory variables and first order
interactions) and compared its performance with submodels from which non-significant terms
were deleted one at a time. Final model selection of fixed effects was conducted using Likelihood
Ratio Tests (Lewis et al. 2010). As the appropriate number of degrees of freedom to use in
assessing statistical significance in mixed-effects models is controversial (Baayen et al. 2008), we
present coefficients of the selected models (from model sets 1, 2 and 4) with estimates of highest
posterior density (HPD) intervals, which are Bayesian equivalents of confidence intervals,
calculated at the 95% level using Markov Chain Monte Carlo (MCMC) sampling with 10,000
samples. In this method, a coefficient is deemed significantly different from zero when the HPD
interval does not include zero. Second, we used MCMC sampling to calculate p-values based on
the posterior distribution. As MCMC sampling is not yet implemented with random intercept
models, we used the t-value to assess the significance of factors (t-values > 2; Burnham and
Anderson 2002) in models with meteorological factors (model set 3).
References
Baayen, R.H. (2008) Analyzing Linguistic Data: A Practical Introduction to Statistics. Cambridge
University Press, Cambridge, UK.
Burnham, K.D. & Anderson, D.R. (2002) Model Selection and Multimodel Inference: a Practical
Information–Theoretic Approach. 2nd edition. Springer-Verlag, New-York.
Lewis, F., Butler, A. & Gilbert, L. (2010) A unified approach to model selection using likelihood
ratio test. Methods in Ecology and Evolution, 2, 155 – 162.
Table S2. Details of the survival sample of captive-bred North African houbara bustards (N = 957 individuals) with body weight,
release age by years, period of release (i.e. period 1 – spring release and period 2 –autumn release) and sex of the equipped birds. The
mean release group size by years is also given.
Male
Year
Release group size Period of release
N
Female
Body weight (g) Release age (days)
N Body weight (g) Release age (days)
2001
10 ± 3
Period 2
Period 1
16
14
1679 ± 149
1765 ± 165
454 ± 177
572 ± 155
12
2
1152 ± 123
1153 ± 47
308 ± 21
721 ± 258
2002
27 ± 10
Period 2
Period 1
31
35
1658 ± 147
1461 ± 107
342 ± 55
137 ± 8
2
39
970 ± 106
1111 ± 112
378 ± 10
140 ± 9
2003
47 ± 21
Period 2
Period 1
17
15
1522 ± 99
1490 ± 207
293 ± 27
166 ± 13
32
42
1073 ± 103
1197 ± 96
275 ± 26
173 ± 16
2004
104 ± 47
Period 1
42
1308 ± 156
134 ± 8
15
942 ± 120
138 ± 9
2005
35 ± 20
Period 2
Period 1
46
33
1393 ± 254
1182 ± 189
207 ± 101
138 ± 20
39
24
1060 ± 135
965 ± 160
231 ± 92
137 ± 20
2006
82 ± 35
Period 2
Period 1
49
21
1558 ± 144
1381 ± 96
284 ± 21
144 ± 10
47
20
1119 ± 137
1021 ± 74
286 ± 25
142 ± 9
2007
133 ± 13
Period 2
Period 1
48
29
1541 ± 135
1522 ± 150
298 ± 13
224 ± 10
72
30
1113 ± 157
1128 ± 108
295 ± 11
225 ± 9
2008
498 ± 0
Period 1
15
1636 ± 158
177 ± 1
15
1123 ± 74
174 ± 2
2009
389 ± 115
Period 2
Period 1
30
49
1204 ± 277
1171 ± 189
183 ± 95
102 ± 20
25
51
943 ± 185
900 ± 135
216 ± 93
101 ± 19
Appendix S3. Goodness-of-fit and structure of the multi-event capture-recapture models
We verified the fit of the general, time-dependent model with program U-CARE version 2.3.2
(Choquet et al. 2009a). Because there is no test available to assess goodness-of-fit (GOF) of the
general model when combining live telemetry locations and dead recoveries, we performed GOF
tests separately on recaptures and recoveries (Duriez et al. 2009). For dead recoveries, GOF tests
indicated an excess of immediate (i.e., next occasion) recoveries (M.ITEC test: χ² = 241.5, df = 66,
P < 0.001) and no excess of recoveries concentrated in some years (M.LTEC test: χ² = 77.2, df =
86, P = 0.7). For live recaptures, the GOF test was highly significant (χ² = 6458, df = 168, P <
0.001), mainly due to a positive trap-dependence effect on live recaptures (i.e., higher capture
probability at j+1 when individuals are captured at occasion j; P < 0.001) that was accounted for
following Pradel and Sanz-Aguilar (2011). We calculated an approximate over-dispersion
coefficient using tests M.ITEC and M.LTEC based on recoveries (Σχ² / Σdf = 318.7 / 154 = 2.07 =
ĉ) for model selection. Therefore, we considered a modified version of the standard CormackJolly-Seber model incorporating trap-dependence effect to account for this lack of fit (Pradel and
Sanz-Aguilar 2011). We considered 4 states: alive individuals captured at t-1 [𝐴𝐶] or not captured
̅̅̅̅̅, just dead [𝐽𝐷] or dead [𝐷] and 3 events to code for the observed fate of an individual
at t-1 [𝐴𝐶]
at each occasion (event 0: not observed, event 1: observed as alive individual, event 2: observed as
dead individual). The tracking data used for this analysis represent a particular (i.e. continuous)
situation violating the general assumptions of a short period in which individuals are recaptured
required by capture-recapture models (Lebreton 1992). However, this assumption can be violated
without biasing survival if recapture rate is >0.2 (O’Brien et al. 2005). We defined an occasion as a
time step of three months, but as only one event can be coded per occasion, the situation was
problematic when two different events occurred within a single occasion (e.g. recaptured then
recovered dead at t). Thus, in that case, we postpone the recovery to the next occasion (t+1) to take
into account that the individual was known to have survived from t-1 to t. To illustrate this, if a
male is released at the first occasion, missed at the second, recaptured at the third occasion and
found dead in the same occasion, the history would be coded ‘1012’. Finally, we considered four
different groups of individuals: Sex (male and female) and period of release (autumn and spring
release).
First, the initial state probability matrix reported the probability of being in a given state when first
encounter. In our case, individuals are alive when released.
States
𝐴𝐶
̅̅̅̅
𝐴𝐶
𝐷
[1
0
0 ]
The transition matrix estimated individual (𝜙𝑡 ) survival. By convention, “from states” are in
columns and “to states” are in rows.
𝐴𝐶
̅̅̅̅
𝐴𝐶
𝐽𝐷
𝐷
𝐴𝐶
Φ
0
1−Φ
0
̅̅̅̅
𝐴𝐶
0
Φ
1−Φ
0
𝐽𝐷
0
0
0
1
[𝐷
0
0
0
1]
The transition matrix estimated detection probabilities, we considered two detection probabilities
at time t ‘P*’ and ‘P’ depending on whether an individual was previously captured or not at the
previous occasion t-1.
𝐴𝐶
̅̅̅̅
𝐴𝐶
𝐽𝐷
𝐷
𝐴𝐶
𝑝∗
1 − 𝑝∗
0
0
̅̅̅̅
𝐴𝐶
𝑝
1−𝑝
0
0
𝐽𝐷
0
0
1
0
[𝐷
0
0
0
1]
Finally, the matrix of event probabilities reported the encounter probabilities.
0
1
2
𝐴𝐶
0
1
0
̅̅̅̅
𝐴𝐶
1
0
0
𝐽𝐷
1−𝑟
0
𝑟
1
0
0]
[𝐷
Event matrix described: 𝑟 : recovery rate of dead individuals.
References
Choquet, R., Lebreton, J.-D., Gimenez, O., Reboulet, A.-M. & Pradel, R.. 2009. U-CARE: Utilities
for performing goodness of fit tests and manipulating CApture-REcapture data. Ecography, 32,
1071–1074.
Duriez, O., Sæther, S.A., Ens, B.J., Choquet, R., Pradel, R., Lambeck, R.H.D. & Klaassen, M.
(2009) Estimating survival and movements using both live and dead recoveries: a case study of
oystercatchers confronted with habitat change. Journal of Applied Ecology, 46, 144-153.
Lebreton, J.-D., Burhnam, K.P., Clobert, J. & Anderson, D.R. (1992) Modeling survival and
testing biological hypotheses using marked animals: a unified approach with case studies.
Ecological Monographs, 62, 67-118.
O’Brien, S., Robert, B. & Tiandry, H. (2005) Consequences of violating the recapture duration
assumption of mark-recapture models: a test using simulated and empirical data from an
endangered tortoise population. Journal of applied Ecology, 42, 1096-1104.
Pradel, R. (2005) Multievent: an extension of multistate capture recapture models to uncertain
states. Biometrics, 61, 442 – 447.
Pradel, R & Sanz-Aguilar, A. (2012) Modeling Trap-Awareness and Related Phenomena in
Capture-Recapture Studies. PLoS ONE, 7(3), e32666.
Figure S2. Partial plot illustrating the interaction between time and release age (given as
quantiles) according to the net dispersal distances of captive-bred North African houbara
bustards (log transformed data were back-transformed).
10
= 243
= 297
4
6
8
= 631
2
Net Dispersal distance (km)
12
Release age values
= 84
= 172
0
20
40
60
Time after release (days)
80
100
Appendix S4. Modelling variation in recovery and recapture rates
The model selection procedure is detailed in tables S3 and S4. Starting with the general model
∗
[𝑆𝑎∗𝑡∗𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑 , 𝑃𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑+𝑡
+ 𝑃𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑+𝑡 , 𝑅𝑎∗𝑡∗𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑 ], we examined whether recovery
and recapture probabilities were time-, time-classes, sex- and/or period dependent. The model
assuming different recovery probability for both time-classes [𝑅𝑎 ] was selected (Table S3).
Estimates of the model indicate higher recovery rates on the short-term ([𝑅𝑎1 ] = 0.98 ± 0.02)
than long-term ([𝑅𝑎2 ] = 0.48 ± 0.03). For alive recaptures, detection probabilities [𝑃 ∗ ] and [P]
were not dependent of sex and period of release (see Table S4). To properly account for the
∗
trap-dependence effect, an additive time effect was kept [𝑃+𝑡
+ 𝑃+𝑡 ]. The probability to be
detected alive in ‘𝐴𝐶’ (i.e. when captured at t-1) ranges between 0.95 ± 0.01 (in 2003) and 0.79
̅̅̅̅ ’ (i.e. when not captured at t-1)
± 0.05 (in 2009). The probability to be detected alive in ‘𝐴𝐶
varied from 0.09 ± 0.03 in 2009 to 0.36 ± 0.07 in 2003.
Table S3. Model selection on recovery probabilities of captive-bred Houbara bustards in the
oriental Morocco from March 2001 to February 2010, performed through QAICc values (with ĉ
= 2.07) and ranked by increasing order, with the best model (lowest QAICc) in bold. Only a
limited number of relevant models are presented here for clarity. Model deviances, number of
estimable parameters (Np), ΔQAICc𝑖 values and QAICc weights (w𝑖 ) are also given. For all
model, survival [𝑆] and detection [𝑃 ∗ + 𝑃] probabilities are time-dependent [S𝑎∗𝑡∗𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑 +
∗
𝑃𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑+𝑡
+ 𝑃𝑠𝑒𝑥∗𝑝𝑒𝑟𝑖𝑜𝑑+𝑡 ].
Model N°
Recoveries (r)
Np
Deviance
QAICc
ΔQAICci
wi
E1
a
86
6437.9
3285.5
0.0
0.42
E2
a*sex
88
6430.5
3286.0
0.5
0.33
E3
a*period
88
6432.1
3286.8
1.3
0.22
E4
a*sex*period
92
6424.1
3291.2
5.7
0.02
E5
a*t
102
6392.1
3296.7
11.2
0.00
E6
a*t*period
116
6364.2
3312.6
27.1
0.00
E7
a*t*sex
119
6369.8
3321.6
36.1
0.00
E8
a*t*sex*period
145
6317.6
3351.5
66.0
0.00
E9
t
93
6604.1
3380.3
94.8
0.00
E10
t*sex*period
118
6458.0
3362.1
76.6
0.00
Notation: p = capture rate; r = recovery rate; t = time-dependent on a yearly basis (see shortcut);
“*” = interaction effect; “+” = additive effect. Subscripts: a (2 time-classes) = Short- (a1) vs.
long-term (a2) such as a1 = from release to the third months post-release, a2 = More than 3
months post-release. Group: sex = individuals are grouped by sex (males and females), period =
individuals are grouped according to their release period. Shortcut: t = t (1 2 3 4, 5 6 7 8, 9 10
11 12, 13 14 15 16, 17 18 19 20, 21 22 23 24, 25 26 27 28, 29 30 31 32, 33 34 35 36).
Table S4. Model selection for detection rates with recovery probabilities [𝑅𝑎 ], ranked by
increasing value of QAICc (with ĉ = 2.07) with the best model in bold. Model deviances,
number of estimable parameters (Np), ΔQAICc𝑖 values and QAICc weights (w𝑖 ) are also given.
Recapture (P*)
if captured at t-1
Recapture (P)
if not captured at t-1
Np
Deviance
QAICc
ΔQAICci
wi
t
t
83
6439.6
3280.0
0.0
0.53
sex+t
sex+t
84
6438.4
3281.5
1.5
0.25
period+t
period+t
84
6439.4
3282.0
2.0
0.19
period*sex+t
period.sex+t
86
6437.9
3285.5
5.5
0.03
Notation: t = time-dependent on a yearly basis. Group: sex and release period (period1 =
autumn and period2 = Spring). “*” = interaction effect. “+” = additive effect
∗
Table S5. Model selection for survival rates (S) with recovery and detection probabilities [𝑃+𝑡
+
𝑃+𝑡 + 𝑅𝑎 ]. Only a limited number of relevant models are presented here for clarity, ranked by
increasing value of QAICc (with ĉ = 2.07), with the best model in bold. Model deviances,
number of estimable parameters (Np), ΔQAICc𝑖 values and QAICc weights (w𝑖 ) are also given.
Model N°
Survival
Np
Deviance
QAICc
ΔQAICci
wi
S1
a1*period1*t+a1*period2+a2*t
32
6550.8
3229.1
0
1.00
S2
a*t*period
49
6511.8
3244.9
15.8
0.00
S3
a*t
31
6626.0
3263.4
34.3
0.00
S4
a1*period1*t+a1*period2+a2
24
6634.3
3253.2
24.1
0.00
S5
a1*t*period*sex+a2*t*period
66
6469.1
3259.1
30
0.00
S6
a1*t+a2*t*period
40
6604.4
3271.3
42.2
0.00
S7
a*t*sex
49
6576.7
3276.2
47.1
0.00
S8
t*sex*period
49
6602.9
3288.9
59.8
0.00
S9
a1*period+a2*t
24
6723.0
3296.1
67
0.00
S10
t*sex
31
6697.7
3298.0
68.9
0.00
S11
a1*period1+a1*period2*t+a2*t
32
6705.3
3303.7
74.6
0.00
S12
a1*period+a2
16
6807.1
3320.5
91.4
0.00
S13
a*sex*period
21
6789.1
3322.0
92.9
0.00
S14
a1*period*sex+a2
18
6802.6
3322.4
93.3
0.00
S15
a
15
6864.9
3346.5
117.4
0.00
S16
i
14
6881.7
3352.6
123.5
0.00
Notation: t = time-dependent on a yearly basis, i = null model. Subscripts: a = Short- vs. Longterm (2 time-classes: a1 and a2). Group: sex and period of release (period1 = autumn and
period2 = spring).
Table S6. Model selection with temporal covariates. Only a limited number of relevant models are presented here for clarity, ranked
by increasing value of QAICc (with ĉ = 2.07), with the best model in bold. Model deviances, number of estimable parameters (Np),
ΔQAICc𝑖 values, QAICc weights (w𝑖 ) and biological interpretations are also given.
Model N°
Temporal covariate model
Np
Deviance
QAICc
ΔQAICci
wi
T1
a1*period1*t*(ADT*ADR)+a2*t*(ADT+ADR)
23
6593.8
3231.6
0.3
0.38
Interaction effect on STS for autumn release and additive effect on LTS
T2
a1*period1*t*(ADT*ADR)+a2*t*(ADT*ADR)
24
6588.9
3231.3
0.0
0.44
Interaction effect of ADT and ADR
T3
a1*period1*t*(ADT+ADR)+a2*t*(ADT+ADR)
22
6607.6
3236.3
4.7
0.04
Additive effect on STS for autumn release and on LTS
T4
a1*period1*t*(ADT)+a2*t*(ADR+ADT)
21
6610.5
3235.7
4.1
0.06
Effect of ADT on STS for autumn release and additive effect on LTS
T5
a1*period1*t*(ADT+ADR)+a2*t*(ADT*ADR)
23
6602.7
3236.0
4.4
0.05
Additive effect on on STS for autumn release and interaction effect on LTS
T6
a1*period1*t*(ADT*ADR)+a2*t*(ADR)
22
6621.9
3243.2
11.6
0.00
Interaction effect on STS for autumn release and ADR effect on LTS
T7
a1*period1*t*(ADT*ADR)+a2*t*(ADT)
22
6633.2
3248.6
17.0
0.00
Interaction effect on STS for autum release and ADT effect on LTS
T8
a1*period1*t*(ADT)+a2*t*(ADT)
20
6650.0
3252.7
21.1
0.00
Effect of the ADT
T9
a1*period1*t*(ADR)+a2*t*(ADR+ADT)
21
7062.3
3311.8
80.2
0.00
Effect of ADR on STS for autumn release and additive effect on LTS
T10
a1*period1*t*(ADR)+a2*t*(ADR)
20
7089.5
3322.3
90.7
0.00
Effect of the ADR
T1*
a1*period1*t*(ADT*ADR)+a2*t*(ADT+ADR)
21
6611.2
3236.3
4.7
0.04
T1 with no hunting effect
T11
a1*period1*t*(hunt)+a2*t*(hunt)
18
6711.1
3278.2
46.6
0.00
Effect of hunting only
Biological interpretations
Notation: ADT = Annual ambient air temperature, ADR = annual daily rainfall, hunt = whether hunting activity occurred or not, STS
= Short-term survival and LTS = Long-term survival. Note that in all the models, hunting activity is considered (except for model
T1*) and [𝑆𝑎1∗𝑝𝑒𝑟𝑖𝑜𝑑2 ] is kept constant (see Model S1, Table S5).
Table S7. Model selection with individual covariates ranked by increasing value of QAICc
(with ĉ = 2.07), with the best models in bold. Model deviances, number of estimable parameters
(Np), ΔQAICc𝑖 values and QAICc weights (w𝑖 ) are given. The corresponding tested survival
parameters (a1*p1 = short-term survival for autumn release, a1*p2 = short-term survival for
spring release and a2 = long-term survival) is given. We precise whether and which parameter
is significant (CI of the slopes that do not include 0).
Model N°
Individual covariates
Sa1*p1*t
Sa1*p2
Sa2*t
Np
Deviance
QAICc
ΔQAICci
wi
Significant terms
1
Ra
-
-
*
34
6534.1
3225.1
0.0
0.52
(Ra)
2
Ra+RGr
-
-
*
35
6533.5
3226.3
1.2
0.29
(Ra)
3
Ra*RGr
-
-
*
36
6532.5
3227.8
2.7
0.14
(Ra)
4
Ra*RGr
*
-
-
37
6545.6
3232.1
7.0
0.02
-
5
Ra*RGr
-
*
-
36
6546.7
3232.7
7.6
0.01
-
6
BC
-
*
-
35
6547.9
3233.2
8.1
0.01
-
7
RGr
-
-
*
34
6551.0
3234.7
9.6
0.00
-
8
BC*Ra
*
-
-
37
6549.8
3234.2
9.1
0.01
-
9
BC*Ra
-
-
*
36
6551.8
3235.1
10.0
0.00
-
10
BC
*
-
-
35
6551.9
3235.2
10.1
0.00
-
11
RGr
*
-
-
35
6559.5
3238.8
13.7
0.00
-
12
RGr
-
*
-
35
6559.5
3238.8
13.7
0.00
-
13
Ra
*
-
-
34
6561.1
3239.6
14.5
0.00
-
14
Ra
-
*
-
34
6561.3
3239.7
14.6
0.00
-
15
BC
-
-
*
35
6635.6
3275.6
50.5
0.00
-
16
BC*Ra
-
*
-
37
6643.5
3279.4
54.3
0.00
-
Note: Individual covariates are Ra = Release age, RGr = release group size, BC = body
condition index, “*” refers to the interaction between covariates and “+” to the additive effect.
The significant term(s) are given in brackets. The estimate and CI of Model 1 is given in the
result section of the main manuscript.