Complex phenological changes and their

consequences in the breeding success of a migratory

bird, the white stork Ciconia ciconia

Oscar Gordo1*, Piotr Tryjanowski2, Jakub Z. Kosicki3 and Miroslav Fulı́n4

1Department of Zoology & Physical Anthropology, Complutense University of Madrid, Jos�e Antonio Novais 2,

E-28040, Madrid, Spain; 2Institute of Zoology, Pozna�n University of Life Science, Wojska Polskiego 71C, 61-625,

Pozna�n, Poland; 3Department of Avian Biology and Ecology, Faculty of Biology, Adam Mickiewicz University,

Umultowska 89, 61-614, Pozna�n, Poland; and 4East-Slovakian Museum Ko�sice, Hviezdoslavova 3, SK-041 36

Ko�sice, Slovakia

Summary

1. The timing of bird migration has shifted in response to climate change. However, few stud-

ies have linked the potential consequences of any phenological shift on individual fitness and

even fewer have disentangled the role of plasticity and microevolution in the observed shifts.

2. The arrival date and breeding success of white storks (Ciconia ciconia) have been recorded

since the 1880s in Slovakia. We used data for two periods (1895–1913 and 1977–2007), whichwere considered, respectively, as populations before and after the start of climate warming.

About 4000 male and 2500 female arrival dates along with 3000 breeding attempts were studied.

3. Mean arrival dates did not differ between the two periods. During 1977–2007, males

tended towards a slight delay for most fractions of arrival distribution. Protandry was

reduced by 30% (1�44 days).

4. In both sexes, the early percentiles of the arrival distribution arrived later those years with

warmer temperatures at the African wintering grounds, while late percentiles advanced their

arrival when temperatures were higher in the European areas flown over during migration.

5. Mean breeding success of the Slovakian population has not changed since 1977. However,

fecundity selection for arrival date reduced over the years: at the end of 1970s and 1980s,

early breeders had more success than late breeders, but this seasonal trend disappeared

towards the end of the study period. An early arrival and territory acquisition may have

become less of an advantage due to the enhancement of feeding opportunities during the

breeding season in recent decades.

6. A century ago, stork arrival varied spatially, with earlier arrivals at low altitudes, southern

slopes and warmer and drier regions. This spatial variation mostly vanished, and at present,

we found little correlations with topographical and climatic gradients.

7. We showed that long-term temporal changes in the timing of biological events may be

complex because each fraction of a population and sex may show different temporal trends in

their arrival dates. In addition, the effect of biotic and abiotic factors may change consistently

in space and time, and thereby phenotypes’ value depends on the circumstances that are

expressed due to its variable fitness consequences.

Key-words: arrival date, climate change, long-term study, migration, plasticity, protandry,

selection, sexual differences, Slovakia, temporal trends

Introduction

Responses of organisms to climate change are becoming

more and more apparent across the globe (Parmesan 2006;

Rosenzweig et al. 2008). Among these, phenology has*Correspondence author. E-mail: ogvilloslada@gmail.com

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society

Journal of Animal Ecology 2013, 82, 1072–1086 doi: 10.1111/1365-2656.12084

received much attention because shifts in the timing of bio-

logical events mirror climate fluctuations. In recent dec-

ades, warmer temperatures are hastening the spring

activities of plants and animals (Root et al. 2005;

Rosenzweig et al. 2008). The arrival of migratory birds

has been a focus of special interest in phenological

research due to negative fitness consequences found in

those populations unable to keep pace with the advance of

the trophic levels on which they rely during their reproduc-

tive period (Both et al. 2006, 2010; Jonz�en, Hedenstr€om &

Lundberg 2007; Møller, Rubolini & Lehikoinen 2008;

Jones & Cresswell 2010; Saino et al. 2011). Overall, spring

arrivals of migrants are advancing by 4 days per decade

(Lehikoinen & Sparks 2010). However, there are notewor-

thy differences among populations (Rubolini et al. 2007;

Gordo & Doi 2012) as a result of the differential environ-

mental pressures to which they are subjected in passage

and wintering areas (Both & te Marvelde 2007; Gordo

2007), and some of their biological characteristics

(Tryjanowski, Ku�zniak & Sparks 2005; Spottiswoode,

Tøttrup & Coppack 2006; Møller, Rubolini & Lehikoinen

2008; V�egv�ary et al. 2010). While it is essential to quantify

accurately any shift in phenological traits (e.g. the number

of days of advance or delay), it is necessary to scale this

into phenological responses in trophic interactions to

determine the adaptive meaning of phenological responses.

Parallel phenological records for competitor species or

lower/upper trophic levels would be necessary to put phe-

nological responses into an ecological context (Visser &

Both 2005; Both et al. 2009; Vatka, Orell & Rytk€onen

2011). Similarly, a causal link needs to be established

between phenology and individual fitness to put phenolog-

ical responses into an evolutionary context (Both & Visser

2001; Both et al. 2006; Post & Forchhammer 2008;

Gienapp & Bregnballe 2012; Lane et al. 2012).

In contrast to research on the temporal trends of bird

migratory phenology, topics such as the geographical var-

iability of arrivals and the progression of the migratory

wave across breeding territories remain poorly studied

(Sparks & Braslavsk�a 2001; Gordo, Sanz & Lobo 2008;

Hulbert & Liang 2012). This imbalance is especially

intriguing within a historical perspective because the aim

of phenological studies of bird migration was originally

the description of the spatial variability in arrival dates

(Southern 1938). Unfortunately, at the time, there were

no suitable tools to properly manage huge phenological

data bases, and the introduction of new study techniques

of bird migration, such as ringing, resulted in a premature

abandonment of this topic (von Haartman & S€oderholm-

Tana 1983). Recent studies have demonstrated that spatial

variability of bird arrivals closely follows climatic and

geographical gradients, suggesting the existence of com-

mon environmental drivers for the spring progression of

migratory birds until their arrival at the breeding grounds

(Gordo, Sanz & Lobo 2007a,b, 2008). Moreover, in some

species, high population density is related to an earlier

arrival phenology (Gordo, Sanz & Lobo 2007b). These

findings suggest complex interactions between the abiotic

and biotic features of the environment affecting each pop-

ulation as well as the existence of spatial heterogeneity

(i.e. among populations) in the strength of selection for

an early arrival date at the breeding grounds (Gordo

2007; Gordo, Sanz & Lobo 2007a, 2008).

Here, we have carried out a comprehensive study of the

migratory phenology of a long-distance migratory bird

species, the white stork (Ciconia ciconia, L.). The popular-

ity of this migratory bird makes the white stork an excel-

lent candidate for large-scale and long-term phenological

studies. In Slovakia, a volunteer-based monitoring scheme

has gathered information about the arrival and reproduc-

tive success of individuals since the 1880s. We used data

for two periods (1895–1913 and 1977–2007) which are

considered, respectively, as populations not subjected to,

and subjected to, recent warming (Lapin 2004; Melo

2005; see Fig. S1, Supporting information). During the

first period, only the arrival of males was recorded, while

in recent decades, monitoring has been enhanced by

including the arrival of the female and the number of

fledged chicks in each nest. In our long-term study, we

investigated whether or not any change in the timing of

migration have been a result of plastic responses to cli-

matic conditions during migration and wintering and how

this affected breeding success of individuals depending on

their arrival time. In addition, we also investigated the

spatial variability of arrivals and the environmental

factors affecting it.

Materials and methods

white stork data

Data consist of 4005 male and 2549 female records of arrivals to

their nesting sites in Slovakia (827 different sites). The arrival

date was defined as the date when individuals were seen occupy-

ing the nest for the first time. Dates were extremely accurate

because they were recorded by volunteers living near the nests or

who visited them on a daily basis (Ful�ın et al. 2009). Our sample

is representative of the Slovakian population owing to the fact

that the overwhelming majority of storks build their nests on

human-made structures, such as chimneys, roofs or electric

pylons (Kalivodov�a, Valachovi�c & K€urthy 1993; Ful�ın et al.

2009). Species misidentification is highly unlikely because of

stork’s popularity and characteristics, which are too distinct from

any other species breeding in Slovakia. Studies with individually

marked storks have demonstrated that males return and occupy

nests before females (Cramp 1977; Tortosa & Redondo 1992;

Barbraud, Barbraud & Barbraud 1999; Kosicki, Sparks &

Tryjanowski 2004). Therefore, the first and the second individuals

recorded for each nest were assumed to be male and female,

respectively. Although there are records for the arrival of storks

to Slovakia since 1882, we only used data for the periods 1895–

1913 and 1977–2007. The volunteer-based monitoring scheme

was interrupted due to the I World War, and it was not resumed

until the mid-1970s. Records of female arrivals were available

only during the later period. The number of fledglings (average

2�43, range 0–6) was available in 2916 breeding attempts during

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

Migratory phenology and breeding success 1073

the period 1977–2007 and was used as a measure of individual

breeding success. White storks do not refrain from breeding once

the nest is occupied. Therefore, when the number of fledglings

was zero, it indicated a true breeding failure between laying and

fledgling.

temporal changes in arrivals

Temporal trends in stork arrivals during the period 1977–2007

were analysed by quantile regression (Cade & Noon 2003) with

year as explanatory variable using Blossom version W2008.04.02

(Cade & Richards 2005). Temporal trends in the width of the dis-

tribution of arrival dates were explored by simple regression of

the annual standard deviation of arrivals against the year to

investigate whether arrival dates have become more synchronous.

Furthermore, we calculated the difference between the arrival

date of the male and the female of each nest and in each year

(n = 2,538) and explored its temporal trend to determine changes

in the degree of protandry (the earlier arrival of males relative to

females). Temporal autocorrelation of data was checked by the

autocorrelation function with a lag from 1 to 15 in all time series,

and the significance of correlations was tested by the Ljung-Box

Q statistic at each lag. We did not find evidence of temporal

autocorrelation in any time series.

Temporal changes in stork phenology over the past century

were assessed by two independent comparisons of male arrival

dates recorded in both study periods. First, a multivariate analy-

sis of variance (MANOVA) was carried out to test for differences in

average arrival dates of the 5th, 10th, 15th, etc. up to the 95th

percentile (total of 19 percentiles as response variables) of each

period (as categorical explanatory variable). Temporal autocorre-

lation was checked by the same procedure previously described,

and we did not find evidence of temporal autocorrelation in any

percentile time series (Fig. S2, Supporting information). In a sec-

ond analysis, we performed a paired t-test for the median arrival

date to 23 localities with records in both periods (Table S1, Sup-

porting information).

temperature and arrivals

Based on accurate information of wintering areas, migration

routes and migration periods of eastern European storks (Fiedler

2001; Berthold et al. 2002; Van den Bossche et al. 2002; Newton

2008), we selected 61 weather stations from southern, eastern and

north-eastern Africa, the Middle East and south-eastern Europe

available from the Global Historical Climatological Network ver-

sion 2 data base (Peterson & Vose 1997; Peterson et al. 1998).

Temperature time series for the period 1895–2003 were gathered

for the following three area/period combinations: January and

February for stations in southern and eastern Africa; February

and March for stations in Sudan and southern Egypt; March and

April for stations located in the Middle East and south-eastern

Europe (Fig. 1). We focused only on temperature because it is

the most influential weather variable for soaring migratory birds

available at this spatio-temporal scale. We calculated average

temperatures in periods of two months to ensure the climate vari-

ables represented departure, passage and/or arrival of the entire

population (i.e. from earliest to latest individuals). Southern and

eastern Africa are the main wintering grounds of eastern

European white storks, while the other areas represent the main

migratory corridor for the eastern flyway. January and February

are departure months from wintering areas, while migration lasts

from February to April through the passage areas from Sudan to

Slovakia (Reed & Lovejoy 1969; Leshem & Yom-Tov 1996;

Newton 2008; Mestec�aneanu & Mestec�aneanu 2010). We used

the same area/periods for both sexes because differences in their

migratory schedule were too small (some days) for the monthly

temporal resolution of our climatic variables.

We adopted a dual approach to study temperature effects on

stork migration. First, we made an accurate station-by-station

assessment of temperature effects and the potential differences

between sexes and study periods. For this aim, the median arrival

date to Slovakia (only possible for females during 1977–2003)

was regressed against temperature time series, and the obtained

slopes (referred to as sensitivity) were mapped and visually

inspected. The difference in the sensitivity within each weather

station was calculated between periods and sexes. When slopes

were negative, values were multiplied by �1 to ensure compara-

bility of differences obtained from negative and positive pairs of

sensitivities to temperatures. Generalized least squares (GLS)

models containing just an intercept were used to test whether or

not the average of the calculated differences was >0. In the model

for the differences between sexes, a positive and significant inter-

cept would imply that females are significantly more sensitive to

temperature than males. In the model for differences between

periods, a positive intercept would imply that males are currently

more sensitive than in the past. Only 11 out of the 58 weather

stations with meteorological records during 1977–2003 had also

records for the period 1895–1913. To account for the spatial

autocorrelation of climate data, we used five types of spatial cor-

relation structures (Gaussian, linear, exponential, spherical and

rational quadratic) in the residuals of the GLS models with the

latitude and longitude of weather stations as covariates (Zuur

et al. 2009). We selected the exponential correlation structure

without the nugget effect because they showed lowest value of

the Akaike Information Criterion (AIC). Tests were one-tailed.

GLS models were carried out with the nlme package version

3.1.102 of R software (R Development Core Team 2011).

In a second approach, we explored temperature effects for the

5th, 10th, 15th, etc. up to the 95th percentile both in males and

females. For this purpose, we calculated a new set of more syn-

thetic temperature variables. Weather stations were grouped fol-

lowing the previously reported spatio-temporal criteria. Therefore,

a single average annual value was calculated for southern and east-

ern Africa, Sudan and southern Egypt, and the Middle East and

south-eastern Europe (Fig. 1). The resulting single temperature

time-series representative of each area/period were used as explan-

atory variables in multiple regression models with the arrival date

of each percentile as the response variable (i.e. 19 independent

models, one for each percentile). An information-theoretical

approach was applied to determine the relative importance of the

seven possible candidate models in each percentile. By a corrected

AIC for low sample size (AICc), the Akaike weight (xi) of each

area/period was calculated and weighted model averaging was

used for parameter estimation (Burnham & Anderson 2002). Due

to the low number of available weather stations in Africa for the

period 1895–1913, we did not run models for this period.

breeding success

The temporal trend during 1977–2007 in the average number of

fledged chicks per nest in the Slovakian population was

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

1074 O. Gordo et al.

determined by linear regression against year. In addition, we

investigated the effect of migratory phenology and climate during

the breeding period on the breeding success at the country-wide

scale. In this analysis, the median arrival dates of males or

females and monthly values of temperature and precipitation dur-

ing May, June and July for Slovakia (Carrascal, Bautista &

L�azaro 1993; Tortosa & Villafuerte 1999; Moritzi et al. 2001;

Jovani & Tella 2004; Olsson 2007) were included as explanatory

variables in multiple regression models where the annual average

number of fledged chicks per nest was the dependent variable.

The support of each competing possible model (27 = 128) was

determined according to an information-theoretical approach

(Burnham & Anderson 2002), as previously described. Those

models with an AICc three units greater than the best model

were excluded from parameter estimation by weighted model

averaging.

To assess the interannual variability of arrival date effects on

individual breeding success, we looked for heterogeneity in the

slopes between arrivals and breeding success among years. A gen-

eralized linear mixed model (GLMM) with arrival date and year

as fixed continuous variables and the number of chicks raised by

each pair as a response variable was used. Fixed variables were

standardized (l = 0, r = 1) prior to the analysis. Year was also

included as a random factor to add a random intercept and slope

to each year (Zuur et al. 2009). The interaction term (year x arri-

val) of fixed variables specifically tested for trends in the relation-

ship between the arrival date and the number of chicks. A

Poisson distribution with a log link function was used in the

gmler function of the lme4 package version 0.999375-42 for R

software (R Development Core Team 2011). The adaptive Gauss-

ian Hermite approximation was used to improve the accuracy of

model fitting (argument nAGQ = 5). Data showed some devia-

tion from a canonical Poisson distribution because breeding suc-

cess records showed underdispersion and had a small excess of

zeros (i.e. breeding failures). This fact invalidates significance esti-

mates (Zuur et al. 2009). We overcame this by simulating the dis-

tribution of the statistics by bootstrapping (Efron 1979). Random

sampling with replacement was conducted within each year to

keep the original analytical design. For each bootstrap sample,

the model was run, and the obtained results were used to calcu-

late the 2�5th and 97�5th percentiles of the bootstrap distribution.

These percentiles form a good approximation of the 95% confi-

dence interval of the estimated parameters of the fixed effects

included in the model. We simulated 10 000 bootstrap samples.

Finally, the selection differential for arrival date was calculated

both for males and females for all years using the following

Fig. 1. Temporal trends of temperatures

between 1977 and 2003 in wintering and

passage areas of white storks breeding in

Slovakia. In the left part of the figure,

mean temperature graphs for each region.

Temperatures were calculated as the aver-

age of all weather stations within each

region during the months indicated in

each case. Red lines show fitted linear

regressions (b = slope, p = significance). In

the right part, there is a map with spa-

tially interpolated trends of temperatures

for all weather stations. Black dots indi-

cate weather stations location. In most

cases, temperatures have increased in

recent decades, but only 5 out of 46 signif-

icantly at a = 0�05. A few stations (12)

recorded a cooling trend (none signifi-

cant). See colour scale bar for correspon-

dence between colour and the magnitude

of slope. Slovakia is highlighted in green.

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

Migratory phenology and breeding success 1075

formula:

Selection differential ¼Pn

i¼1 ðxi � friÞ=Pn

i¼1 fri� �� �x

s

where xi is the arrival date of an individual i (male or female) in

a certain year, fri is the relative number of chicks fledged by the

individual i; �x is the median arrival date of the population (of

males or females) in the same year, and s is the standard devia-

tion of the arrival dates for that year. The fri was calculated as:

fri ¼ fiPn

i¼1fin

where fi is the number of chicks fledged by an individual i in a

certain year, and n is the number of individuals breeding that

year. We calculated the relative number of fledglings to avoid the

potential scale effect of the different reproductive output attained

each year in the population. In the same way, we scaled the selec-

tion differential to the standard deviation of the trait to get a

comparable magnitude of the deviation of the weighted arrival

date with regard to the median arrival date among years. The

more negative the selection differential, the higher the relative

breeding success for early vs. late arriving individuals (van

Noordwijk, McCleery & Perrins 1995). The temporal trend of

selection differentials was studied by a multiple regression model

with year, sex and their interaction as explanatory variables. The

interaction between year and sex tested specifically for the homo-

geneity of the slopes between males and females.

spatial patterns and modelling of arrivals

A grid layer for Slovakia with a resolution of 0�042º (which cor-

responds to 150 s, or ~4�7 km) was created for the study of spa-

tial migratory patterns of white storks. The median date of all

arrival dates in each cell was calculated. Three different pheno-

logical maps were generated: (i) male arrivals during 1895–1913;

(ii) male arrivals during 1977–2007; and (iii) female arrivals dur-

ing 1977–2007. Arrivals were modelled using a set of 11 explana-

tory variables, which were calculated for each cell using Idrisi 32

software (Clark Labs 2001). Variables were as follows: mean alti-

tude, altitude range, terrain slope, aspect (mean direction of the

slope), annual mean temperature, annual range of temperature,

annual sum of precipitation, precipitation seasonality (coefficient

of variation), white stork breeding population density, latitude

and longitude. Topographical and climatic variables are related

and represent important predictors of spatial patterns of arrival

dates of migratory birds (Sparks & Braslavsk�a 2001; Gordo, Sanz

& Lobo 2007a,b, 2008). We expected these variables to be rele-

vant because there are marked topographical gradients which

impose highly contrasting climatic conditions across Slovakia in

spite of its relatively small area (c. 49 000 km2). White stork

breeding population density was calculated as the number of

nests in a radius of 15 km around the centre of each cell found

in the last national census of Slovakia carried out in 2004 (M.

Ful�ın, unpublished data). A previous study demonstrated that

storks in Iberia breeding in higher density areas arrived earlier on

average (Gordo, Sanz & Lobo 2007b). This variable was included

only in models for the 1977–2007 period because the storks have

spread throughout Slovakia during the last century, and conse-

quently, the distribution of 2004 is not representative of past

times. The resampling radius of 15 km around each cell was used

to match the 10 9 10 km resolution of the 2004 national census

data. Latitude and longitude together with their interaction were

included to account for other potential spatial gradients (Legen-

dre & Legendre 1998). We did not include polynomial terms in

any of the predictors because a preliminary exploration of the

relationships with arrival data did not show non-linear relation-

ships. All explanatory variables were standardized (l = 0, r = 1)

prior to analyses.

We applied Partial Least Square Regression (PLSR; Carrascal,

Galv�an & Gordo 2009) for modelling the spatial variability of

arrivals. PLSR combines original predictors into a number of

orthogonal components designed ad hoc to maximize the vari-

ance explained in the response variable. Components account for

successively smaller portions of variance in the response vari-

ables, and consequently, original multidimensionality can be

reduced to one or a few relevant components. Components were

computed by non-linear iterative partial least squares (NIPALS

algorithm), and their significance was established by cross-valida-

tion. Components are interpreted by the weights for each of the

original predictors. Weights provide the magnitude and sign of

the effect of each original explanatory variable in the PLSR com-

ponents. We used weights to make comparisons about the rela-

tive contribution of each predictor in the models obtained for

each sex and period because the sum of their squares is equal to

1 (see details in Carrascal, Galv�an & Gordo 2009). To improve

the comparability between the male arrival model of 1895–1913

and that of 1977–2007, the latter model was repeated without the

nest density variable and only with those localities to the east of

19�6°E longitude. We selected those sites from the eastern half of

the country because our data set for the period 1895–1913 only

has records eastwards from 19�6°E longitude. Differences in the

spatial extent of data sets may influence the relative importance

of explanatory variables in biological gradients (Chust et al.

2004; Rahbek 2005). Residuals were examined for possible spa-

tial structure by calculating Moran’s I autocorrelation coefficient

with a Bonferroni-corrected significance level (Rangel, Diniz-Fil-

ho & Bini 2006) against twenty classes separated by a lag dis-

tance of 30 km (from 30 to 600 km). These analyses were

conducted with GS+ version 5.3.2.

Most of the statistical analyses were conducted with Statistica

version 7; otherwise, the specific software and version has been

cited.

Results

temporal changes in phenology

Males arrived on average the April 1 (9�40SD, range

March 12 to May 2), while females arrived the April 5

(10�32SD, range April 5 to May 15). During the period

1977–2007, all fractions of the Slovakian population of

white storks tended to delay arrival both in males and

females (Fig. 2). However, the confidence intervals

showed that these tendencies were statistically different

from zero at a = 0�05 only for those percentiles earlier

than the 70th in males and only between the 5th and 15th

percentiles in females. The annual standard deviation of

the arrival dates showed a negative tendency both in

males (slope = �0�086, t29 = �1�760, P = 0�089) and

females (slope = �0�058, t29 = �1�528, P = 0�136). There-fore, the arrival date distribution tended to be narrower

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

1076 O. Gordo et al.

because early fractions of the population delayed their

arrival more than late fractions.

During the period 1977–2007, males arrived on average

4�7 days earlier than their females to the nests, but this

difference decreased by 1�44 days in the last three decades

(slope = �0�048 days/year t29 = �2�074, P = 0�047).Arrival dates of males did not differ significantly

between 1895–1913 and 1977–2007 (MANOVA: Wilks

k = 0�678, F18,31 = 0�817, P = 0�668), although late frac-

tions of the population during the latter period tended to

arrive earlier than a century ago (Fig. S3). Furthermore,

median arrival dates observed in those localities with

records in both periods were not significantly different

(paired t-test: t22 = 0�53, P = 0�599).

temperature effects on arrival dates

Regression coefficients (slopes) from simple regressions

between the median arrival date to Slovakia during 1977–

2003 and temperatures in the weather stations of the win-

tering and passage areas of Africa and Europe showed

similar spatial patterns in both sexes (Fig. 3; Table S2).

Storks arrived earlier in years with warmer springs in

south-eastern Europe and in the Middle East [range of

the correlation coefficients (r) for males: �0�268(P = 0�176) to �0�582 (P = 0�001); for females: �0�220(P = 0�270) to �0�651 (P < 0�001)]. Interestingly, the

strongest effect of and sensitivity to temperature was

found in the three nearest stations to the Bosphorus

Straits, a bottleneck in the migratory route of the eastern

European populations of soaring birds (Fiedler 2001; Van

den Bossche et al. 2002; Newton 2008) (average for males:

r = �0�533, slope = �1�12 days/°C, P = 0�004; average

for females: r = �0�615, slope = �1�66 days/°C,P < 0�001]. High temperatures from Kenya to South

Africa were related to later arrivals at the breeding

grounds, but only a few of the correlations from the

southernmost sites were statistically significant [range of r

for males: 0�126 (P = 0�531) to 0�525 (P = 0�005); for

females: 0�1445 (P = 0�472) to 0�464 (P = 0�015); see

Table S2]. Females were on average 0�308 days/°C more

sensitive to climate than males (GLS model: t57 = 2�054,P = 0�022).The effect of temperature on male arrivals was similar

between periods: negative in Europe near to Slovakia and

positive in the wintering areas of east and south Africa

(Fig. 3a vs. b). Sensitivity was on average 0�376 days/°Clower during the period 1895–1913 compared with the

later period, but differences were not statistically signifi-

cant (GLS model: t10 = 0�636, P = 0�269).The analysis of percentiles showed that the temperature

effect was not homogeneous over all fractions of the popu-

lation (Fig. 4). In agreement with the general pattern

found for the median arrival date (Fig. 3), most percentiles

advanced their arrivals in years with warm springs in the

Middle East and south-eastern Europe and delayed their

arrivals after warm winters in eastern and southern Africa

(see regression coefficients in Fig. 4c,d). These regions

were the most important, while the effect of temperature

in the passage region of southern Egypt and Sudan was

negligible both for males and females (note the low Akaike

weights and the regression coefficients close to zero in the

Fig. 4a,b). However, the relative importance of tempera-

tures in eastern and southern Africa vs. the Middle East

and Europe varied according to the percentile examined.

Late percentiles were markedly influenced by temperatures

in the last section of the route, while early percentiles were

more influenced by temperatures in the African departure

areas. Such differences in the relative influence of each

part of the route for early and late individuals were even

apparent in a comparison between males and females.

Males are somewhat earlier than females, and interest-

ingly, the relative importance of temperature in departure

areas in the male models was higher in later percentiles

than in the female models (Fig. 4c,d). However, tempera-

tures in the last part of the route showed higher Akaike

weights for females than for males. Finally, the explana-

tory capacity of multiple regression models had similar

magnitudes and diminished in later percentiles both in

males and females (Fig. 4e). The best fitted models

(r2 > 30%) were obtained between the 20th and 30th per-

centiles.

Quantile

–0·2

–0·1

0·0

0·1

0·2

Slo

pe y

ear (

d·yr

–1) Males

0 20 40 60 80 100 0 20 40 60 80 100Quantile

–0·2

–0·1

0·0

0·1

0·2

Slo

pe y

ear (

d·yr

–1) Females

Fig. 2. Temporal trends of male and female white stork arrivals to their nests in Slovakia during the period 1977–2007. Values are esti-

mated slopes from quantile regression with year as an explanatory variable. Negative values are advancements and positive values are

delays. Stippled lines indicate 95% confidence intervals.

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

Migratory phenology and breeding success 1077

temporal trends and climate effect inbreeding success

The annual average number of fledglings per nest did not

change from 1977 to 2007 (r = 0�141, t29 = 0�743,P = 0�464) but was significantly correlated with both the

annual median male (r = �0�581, t29 = �3�709, P < 0�001)and female (r = �0�557, t29 = �3�306, P = 0�003) arrival

dates (Fig. 5). This single effect of arrivals was not medi-

ated by climatic conditions during the breeding period,

since the best multiple regression models for the breeding

success included both arrival dates and temperatures in

spring (Table 1). Warm temperatures in May also

enhanced the productivity of storks.

Whereas in the past early arrival positively affected

reproductive success, in recent years, reproductive success

is mostly unrelated to arrival time (GLMM interaction

arrival x year was positive, Table 2; see Figs S4 and S5).

This result can also be presented as a positive trend in

selection differentials on arrival dates in males (r = 0�508,t29 = 3�046, P = 0�0049) and females (r = 0�522,t29 = 3�038, P = 0�0050) during the period 1977–2007

(Fig. 6). Differences in the slope values between sexes

were not statistically significant (GLM interaction year x

sex: F1,58 = 1�194, P = 0�279).

patterns of spatial variabil ity

Storks in the period 1895–1913 arrived consistently early

at sites of low altitude, and with colder, moister and more

marked seasonality in local climate (Table 3). The sum of

the two components accounted for up to 47%, which sug-

gest a marked spatial variability in arrival dates (Fig. 7a).

In recent decades, the topographical and climatic gradi-

ents had similar effects on arrival dates both in males and

females (Table 3), but the explained variance declined

strongly (r2 < 5%), resulting in a spatial variability with-

out any evident pattern (Fig. 7b, c).

Differences in the explained variability (r2) became even

greater when the model for male arrivals during 1977–

2007 was rerun by including only records from the eastern

part of Slovakia and excluding the effect of population

density (Table 3). Despite such manifest difference,

weights for the first component were alike (Spearman

rank correlation between variable weights: rS = 0�645,t9 = 2�535, P = 0�032), and the relative percentage of

explained variability by each group of variables was con-

sequently similar during both periods (Table 3). Spatial

autocorrelation was not detected in the residuals from

any of the models (Fig. S7).

Discussion

Many recent studies have devoted special attention to the

impact of climate change on bird migratory phenology

(Lehikoinen & Sparks 2010). Usually, they have focused

on the study of temporal trends of arrival dates of first

individuals and the effect of weather at the study site

(Gordo 2007). Applying this habitual approach, we would

reach few conclusions about the migratory behaviour of

(a) (b) (c)

Fig. 3. Maps of the regression coefficients

between median arrival dates of storks in

Slovakia and temperature time series in

their wintering and passage areas. Map

(a) is arrivals of males during 1893–1915,(b) arrivals of males during 1977–2003and (c) arrivals of females during 1977–2003. Each dot represents a weather sta-

tion. Regression coefficients have been

interpolated among stations to create a

continuous surface of spatial variation

and improve visualization. Slovakia is

highlighted in green. See colour scale bar

for correspondence between colour and

the magnitude of slope.

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

1078 O. Gordo et al.

the Slovakian storks during the last century: they have

not shifted their arrivals because they have not responded

to the increase in temperature in Slovakia. A comprehen-

sive analysis of phenological data by studying the entire

arrival distribution of the population, differences between

sexes, links with reproductive success, and the spatial pro-

gression of spring arrival has discovered complex and to

some extent cryptic changes in its phenology.

In contrast to stork populations from Poland (Ptaszyk

et al. 2003), Spain (Gordo & Sanz 2006) and Lithuania

(Zalakevicius et al. 2006), the overall schedule of arrivals

to Slovakia has not changed directionally in the long

term. However, we did observe differential trends within

different fractions of the population (e.g. early vs. late

individuals, or males vs. females). In the studied species,

the shape of the arrival date distribution has become nar-

rower because early individuals are delaying their arrivals

more than late ones. First arrival dates have been criti-

cized because they have been shown not to fully represent

the behaviour of the whole population (Tøttrup, Thorup

& Rahbek 2006; Miller-Rushing et al. 2008). Our results

suggest that this problem may extend to trends in mea-

sures of central tendency (mean or median), because they

may not take into account changes in the width of distri-

butions of phenological dates. This fact highlights the

necessity of a thorough analysis of the entire distribution

of arrival dates to understand how phenological date dis-

tributions move within the calendar and also how they

vary in their shape (Gordo & Sanz 2009).

Temperature effects on arrival date suggest that storks

have some plasticity to adjust their migration to condi-

tions encountered en route. Theoretically, this was

Fractions of the population (%)

0·10·20·30·40·50·60·70·80·91·0

Var

iabl

e w

eigh

ts (ω

i)

Region 1 Region 2 Region 3

Males

Fractions of the population (%)

–2

–1

0

1

2

3R

egre

ssio

n co

effic

ient

(day

s/ºC

)

Males

5 15 25 35 45 55 65 75 85 95

Var

iabl

e w

eigh

ts (ω

i)

Fractions of the population (%)

0·10·20·30·40·50·60·70·80·91·0

Females

5 15 25 35 45 55 65 75 85 95

5 15 25 35 45 55 65 75 85 95Fractions of the population (%)

–2

–1

0

1

2

3

Reg

ress

ion

coef

ficie

nt (d

ays/

ºC)

Females

5 15 25 35 45 55 65 75 85 95

5 15 25 35 45 55 65 75 85 95Fractions of the population (%)

0·05

0·10

0·15

0·20

0·25

0·30

0·35

0·40

r 2

MalesFemales

(a) (b)

(c)

(e)

(d)

Fig. 4. Results for multiple regression

models between white stork arrivals dur-

ing 1977–2007 and climatic variables in

wintering and spring migration areas. The

explanatory variables were temperatures

in the Middle East and south-western

Europe (Region 1), Sudan and southern

Egypt (Region 2), and southern and east-

ern Africa (Region 3). All the possible

models were made for each percentile and

ranked according to the corrected Akaike

Information Criterion (AICc). The Akaike

weights (xi) for each variable were calcu-

lated and are plotted in the upper graphs.

The parameters of each explanatory vari-

able (mid graphs) and the explanatory

capacity of models (r2; bottom graph)

were estimated according to model averag-

ing and are plotted for each percentile of

the population. All results are shown both

for males and females.

Ave

rage

num

ber o

f fle

dglin

gs

26-Mar 1-Apr 7-Apr 13-Apr

Median arrival date

1·8

2·0

2·2

2·4

2·6

2·8

3·0

3·2MalesFemales

Fig. 5. Relationship between the annual mean number of fledged

chicks and the annual median arrival date of males and females

of the Slovakian population of white storks. Lines comes from a

simple linear regression model.

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

Migratory phenology and breeding success 1079

expected because soaring birds are less controlled by an

endogenous programme to provide individuals with a

more flexible environment-dependent behaviour during

migration (Berthold et al. 2002; Shamoun-Baranes et al.

2003; Newton 2008; Mestec�aneanu & Mestec�aneanu

2011). Interestingly, such plasticity varied among fractions

of the population and even slightly between sexes (Fig. 4).

We may speculate that early individuals (i.e. more

affected by climate) represent older and more experienced

birds (Vergara, Aguirre & Fern�andez-Cruz 2007), which

are able to profit favourable weather conditions to

increase their migratory progression. On the other hand,

the stragglers could be young or solitary individuals (Reed

& Lovejoy 1969), which rely more on their internal cues

or cannot take advantage of social interactions, respec-

tively (Liechti, Ehrich & Bruderer 1996; Chernetsov,

Berthold & Querner 2004).

The opposite effect of temperatures at departure time

and in the last part of the migratory route concurs with

the ecology of most trans-Saharan species. Heat in the

wintering arid grounds of eastern Africa is associated with

drier and more restrictive conditions for feeding (Tøttrup

et al. 2012). However, no or no substantial, fattening or

hyperphagia have been observed in the white stork during

migration (Van den Bossche et al. 2002; Newton 2008;

Zwarts et al. 2009). Thus, migration onset, while notably

variable among individuals and among years within the

same individual (Berthold et al. 2002; Van den Bossche

et al. 2002), does not seem constrained by physiological

preparation depending on food resources. In spite of this

fact, ecological restrictive conditions induced by heat and

drought at the end of the wintering phase in Africa may

affect stork migration by two mechanisms linked to food

availability. The winter distribution of eastern storks in

Africa is related to insect availability (Verheyen 1950;

Dallinga & Schoenmakers 1989; Van den Bossche et al.

2002; Zwarts et al. 2009), such locusts, which are strongly

dependent on the amount and timing of rainfalls

(Todd et al. 2002). Food is erratic, both spatially and

Table 1. Multiple regression models for the annual average number of fledged chicks in the Slovakian population of storks (1977–2007).The explanatory variables were the median annual arrival date (of males or females), and monthly temperature and precipitation during

May, June and July. Only those models with an increase of AICc (DAICc) below 3 are shown. The AICc and the weight (xi) of each

model are also shown. The weights (x), parameters (b) and standard errors (SE) for the explanatory variables are shown at the bottom

of the tables. The explanatory capacity (R2) of models is in the right-bottom corner of tables

Model Arrival date

Temperature Precipitation

AICc AAICc xi,May June July May June July

Males

1 X X 3.937 0.000 0.159

2 X X X 5.198 1.261 0.084

3 X 5.663 1.726 0.067

4 X X X 6.367 2.430 0.047

5 X X X 6.460 2.522 0.045

6 X X X 6.487 2.549 0.044

7 X X X 6.525 2.588 0.043

x 0.910 0.671 0.294 0.218 0.213 0.225 0.245

b �0.047 0.055 �0.008 0.001 9 9 10�5 �3 9 10�5 8 9 10�5 R2 = 0.3341

SE 0.017 0.027 0.008 0.003 4 9 10�4 4 9 10�4 2 9 10�4

Females

1 X X 1.045 0.000 0.159

2 X X X 3.332 2.286 0.051

3 X 3.509 2.463 0.046

4 X X X 3.514 2.469 0.046

5 X X X 3.532 2.487 0.046

6 X X X 3.572 2.527 0.045

7 X X X 3.663 2.618 0.043

x 0.942 0.664 0.231 0.251 0.290 0.256 0.223

b �0.044 0.055 �0.001 0.001 4 9 10�4 �l 9 10�4 �2 9 10�5 R2 = 0.4218

SE 0.013 0.026 0.004 0.003 7 9 10�4 4 9 10�4 2 9 10�4

Table 2. Results for the fixed effects of the generalized linear

mixed model for the breeding success of storks in Slovakia,

1977–2007. The model also included a random intercept and

slope effect of the arrival date within each year. P-values repre-

sent the probability of 0 in the bootstrap distribution of the

estimates. N = 2844 for males and 2199 for females

Fixed effects Estimate

Bootstrap 95 %

PConfidence interval

Males

Arrival date �0.0629 (�0.0828, �0.0434) <0.0001Year 0.0162 (�0.0021, 0.0372) 0.0847

Arrival date 9 Year 0.0369 (0.0162, 0.0580) 0.0008

Females

Arrival date �0.1126 (�0.1355, �0.0885) <0.0001Year 0.0242 (�0.0013, 0.0432) 0.0756

Arrival date 9 Year 0.0288 (0.0080, 0.0553) 0.0107

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

1080 O. Gordo et al.

temporally, and storks exhibit a nomadic behaviour

searching for rain-related outbreaks of insects and thus

for the best foraging sites (Newton 2008; Zwarts et al.

2009). During warmer (and drier) years in East Africa,

storks spend the winter in larger proportion in the south-

ernmost wintering areas following the monsoon peak,

which progresses southwards from the equator during the

austral summer (Zwarts et al. 2009). Thus, late arrivals

preceded by warm temperatures in eastern and southern

Africa during the winter would represent longer migratory

journeys as consequence of an overall more southern win-

ter distribution. In addition, food scarcity may act directly

on the physical condition of individuals by starvation. If

ecological conditions during the winter have been restric-

tive, individuals may need to spend longer periods in the

stopover sites en route to ensure their survival until their

breeding areas (Gordo 2007; Tøttrup et al. 2012). Since

journeys are longer and probably more exhausting for

individuals and/or individuals may be already in poor

body condition, these phenomena may have carry-over

effects on breeding success (Berthold et al. 2002; Zwarts

et al. 2009). This may explain why population productiv-

ity was lower in late years (Fig. 5) independently of

weather condition in Slovakia during reproduction.

In the Middle East and southeastern Europe, warm tem-

peratures at the beginning of the spring are linked to

improved migration conditions (Zalakevicius et al. 2006;

Both & te Marvelde 2007; Gordo 2007; Gordo & Sanz

2008; Tøttrup et al. 2008). This means for a soaring bird

more favourable conditions for flight thanks to

increased thermal convection (Liechti, Ehrich & Bruderer

1996; Shamoun-Baranes et al. 2003; Mestec�aneanu &

Mestec�aneanu 2011). Therefore, the last part of the migra-

tory route could be covered in fewer days by increased

distances flown every day and/or minimal delay in crossing

barriers such as the Jubal or Bosphorus Straits.

The small differences in temporal trends between the

sexes are probably caused by a different influence of cli-

mate along the migratory route led to a decrease in the

degree of protandry in the Slovakian stork population.

This empirical evidence contrasts with the predicted

increase in protandry in migratory birds under a global

warming scenario (Møller 2004; Spottiswoode, Tøttrup &

Coppack 2006; but see Rainio et al. 2007; Tøttrup &

Thorup 2008; Baub€ock et al. 2012). However, our obser-

vation is expected if directional selection towards earlier

arrival dates is relaxed. Currently, storks have no pressure

to arrive early because first arrivals do not provide bene-

fits in terms of increased number of offspring. This relaxa-

tion of selective pressures on arrival date was stronger in

males (e.g. compare the magnitude of the interaction of

the arrival date with the year in Table 2) than in females.

This concurs with the observed stronger delay of arrivals

in males than in females.

The observed relaxation in the effect of arrival on

breeding success suggests that the timing of breeding in

storks does not require an accurate synchronization with

the seasonality of the environment at their breeding

grounds, in contrast to some migrants (Both & Visser

2001; Both et al. 2006, 2009; Jonz�en, Hedenstr€om &

Lundberg 2007; Saino et al. 2011; Gienapp & Bregnballe

2012). Breeding success in the white stork depends on the

absolute abundance of food during spring (Moritzi et al.

2001; Tortosa, Caballero & Reyes-L�opez 2002; Tryjano-

wski & Ku�zniak 2002; Denac 2006a) and weather-related

mortality of chicks (Carrascal, Bautista & L�azaro 1993;

Tortosa & Villafuerte 1999; Jovani & Tella 2004; Olsson

2007) instead of a temporal match with seasonal peaks of

resources, as in other bird species (van Noordwijk,

McCleery & Perrins 1995; Both & Visser 2001; Visser,

Both & Lambrechts 2004; Nussey et al. 2005; Pearce-

Higgins, Yalden & Whittingham 2005; Vatka, Orell &

Rytk€onen 2011). Nevertheless, a seasonal decline in the

breeding success has been observed in some populations

(Tortosa, P�erez & Hillstr€om 2003; Tryjanowski et al.

2004; Tryjanowski & Sparks 2008; but see Grishchenko

2006), and we indeed found this date-effect in some years.

Such differences in the breeding success between early and

late individuals are due to differences in age, experience,

quality and status rather than to a within-season decline

of food supplies (Vergara, Aguirre & Fern�andez-Cruz

2007; Ful�ın et al. 2009).

The relaxation of selection on arrival date may be a

consequence of a homogenization of the quality of breed-

ing territories. The organized map of arrivals shown by

storks one century ago (Fig. 7a) could be reflecting a des-

potic distribution of individuals across Slovakia. The ear-

liest, which are the best phenotypically individuals, would

occupy the best territories and would have higher repro-

ductive success (Vergara & Aguirre 2006). The best and

most productive territories could be placed in those war-

mer and driest areas, and for this reason, the earliest indi-

viduals were recorded there one century ago (Table 3).

This seems plausible since a cold and moist climate may

be limiting the breeding success by a trade-off in the time

budget between provisioning and chicks’ sheltering from

weather (Moritzi et al. 2001). However, this trade-off

could disappear or, at least, could become of little rele-

vance for individuals, if balance between weather and

food supplies has changed. Climate change in Slovakia

during the last century (see Fig. S1; Lapin 2004; Melo

2005) may have improved weather conditions during the

breeding season, especially in the formerly lower quality

territories (colder and wetter), by reducing the adults’

time invested in sheltering and enhancing chick survival.

In addition, food is probably more accessible at present

due to exploitation of rubbish dumps (Tortosa, Caballero

& Reyes-L�opez 2002; Tortosa, P�erez & Hillstr€om 2003;

Kruszyk & Ciach 2010), changes in livestock farming

practices (Tryjanowski, Jerzak & Radkiewicz 2005),

changes in agricultural landscape (Dallinga & Schoenmak-

ers 1989) or reduction in the use of pesticides (Newton

2008), providing feeding opportunities to all breeding

pairs over their necessary threshold for rear their

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

Migratory phenology and breeding success 1081

offspring. If food is no longer a constraint, weather

impact on breeding may become minimal (Denac 2006a),

and consequently, any variation in territory quality based

on climate may disappear. Competition for territory occu-

pation by early arrivals would no matter anymore, and

some expected consequences would be a relaxation of

selection for early arrivals, reduction in protandry, and

finally a free distribution of individuals according to their

arrival phenology, that is, what we find currently

(Fig. 7b,c).

The white stork spreading during the last decades

(Sæther et al. 2006; Ful�ın et al. 2009) supports the idea of

an increase in the carrying capacity of Slovakia by an

enhancement of resources availability. However, it can be

hypothesized that the observed loss of the spatial organi-

zation of arrival dates is linked to dispersal processes.

During the early stages of range expansion and occupa-

tion of new territories, newly established individuals may

express a migratory phenotype not fully suited to the local

conditions. This process seems especially plausible in the

white stork, since distances reported for natal dispersal in

this species (Chernetsov et al. 2006; Kania 2006; Olsson

2007) would easily overcome the strong, but small-scale

(i.e. hundreds of kilometres), environmental gradients of

Slovakia. In fact, dispersal from core breeding areas has

been postulated as a way for the notable recuperation of

the European stork population since the 1980s (Zwarts

et al. 2009). In addition, dispersal would be more frequent

in a growing population since it is a denso-dependent

process (Itonaga et al. 2011). Such decoupling between

1980 1985 1990 1995 2000 2005Year

–0·3

–0·2

–0·1

0·0

0·1

0·2S

elec

tion

diffe

rent

ial f

or a

rriv

al d

ates

Males Females

Fig. 6. Temporal trend of selection differentials for arrival dates

in white storks. Data for males and females are shown sepa-

rately.

Table 3. Results of the four partial least square regression models of spatial variation in arrival dates per grid cell in Slovakian storks.

For each variable, the weight in each component is shown (significant in bold). The explanatory capacity (r2) and significance (P) of each

component are also shown. At the bottom of the table, the percentage of the variance explained by each group of variables in each com-

ponent is given. The ‘subset’ model for males during the period 1977–2007 included only data to the east of longitude 19�6°E and did

not include nest density to improve comparison with the male model for 1895–1913 (see methods for details)

Variable

Between sexes Between periods

Male 1977–2007 Female 1977–2007Male 1895–1913

Male 1977–2007 (subset)

1 st Component 1 st Component 1 st Component 2nd Component 1 st Component

Topographical

Altitude (mean) 0.311 0.398 0.379 �0.028 0.415

Altitude range 0.061 0.269 0.152 �0.231 0.144

Aspect �0.093 �0.168 0.250 0.786 �0.175

Slope 0.116 0.194 0.132 �0.154 0.181

Climatic

Temperature (annual mean) �0.237 �0.362 �0.399 �0.046 �0.328

Temperature range �0.416 �0.399 �0.387 �0.046 �0.300

Precipitation (annual sum) 0.155 0.221 0.425 0.349 0.103

Precipitation seasonality 0.377 0.313 0.276 �0.039 0.465

Biological

Number of nests (in 15 km) �0.324 �0.377

Spatial

Longitude �0.431 �0.229 �0.240 0.227 �0.406

Latitude 0.191 0.186 0.304 0.229 0.143

Longitude 9 Latitude �0.394 �0.193 �0.194 0.269 �0.366

r2 0.037 0.045 0.356 0.104 0.029

P < 0.001 < 0.001 < 0.001 0.014 0.002

% of explained variability

Topographical 12.3 29.7 24.7 69.6 25.6

Climatic 39.5 43.7 56.6 12.7 42.5

Biological 10.5 14.2

Spatial 37.7 12.4 18.8 17.7 31.9

© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society, Journal of Animal Ecology, 82, 1072–1086

1082 O. Gordo et al.

arrival dates of incomers and local conditions, as a result

of the spread of individuals, would modify the spatial pat-

terns of arrival dates, reflecting a transitory situation in

the evolutionary history of a migratory species. One could

expect that natural selection may create spatial gradients

in phenology once again in the future by purging those

individuals mistimed with their environment. However,

this does not seem plausible because of the following rea-

sons: (i) The notion of phenological mismatching is not

relevant for this species, which has a long-lasting breeding

period and does not rely in seasonal food peaks for breed-

ing. (ii) We have demonstrated that selection on the arri-

val date is relaxing. Therefore, it is hard to imagine how

spatial patterns would be re-established in future genera-

tions, if most of the variability in the arrival dates is not

related to a differential reproductive success of individu-

als. (iii) Storks exhibit a notable behavioural plasticity for

adapting to local environmental conditions, for instance

by breeding in larger territories (i.e. diminishing

population density; Barbraud, Barbraud & Barbraud

1999; Denac 2006b), seeking the most appropriate land-

scape structure in the areas surrounding the nest (Denac

2006a) or simply improving their experience with age

(Vergara & Aguirre 2006; Vergara et al. 2006; Vergara,

Aguirre & Fern�andez-Cruz 2007). Finally, although some

classic experiments with storks demonstrated the genetic

basis of bird migration (Sch€uz et al. 1971), we still do not

know anything about the genes controlling the migratory

phenology of this species, how much heritable they are,

and how the interaction genotype environment is for

determining the expressed phenotype.

In conclusion, long-lived animals, such as the white

stork, are expected to rely more on their plasticity than

microevolutionary responses on selection to adapt to

rapid changes in their environment (Morris et al. 2008).

We showed that different parts of the population vary in

their response to environmental changes that differ both

in space and time of the annual cycle. Fitness conse-

quences of variation in arrival date diminished over time,

which likely was the result of ecological changes at the

breeding grounds (homogenizing spatial variation), rather

than to changes in synchrony with other trophic levels.

Acknowledgements

We thank all volunteer observers who participated in this study (www.

bociany.sk). Christiaan Both and three anonymous referees carried out a

thorough review that greatly improved the manuscript. Tim Sparks, Emma

Nelson and Sarah Young help us with English editing. The study was par-

tially supported by a grant from the Polish Ministry of Science N N304

078035 to P.T. O.G. received a contract of the Juan de la Cierva program

(ref. JCI-2009-05274).

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Handling Editor: Christiaan Both

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SUPPORTING INFORMATION

Fig. S1. Temporal trend of temperature in Slovakia during March and April (period of arrivals of storks) between 1895 and 2003. Temperatures are represented as departures from average temperature for the period 1960-1991. Overall, temperature increased 0.81ºC during the study period (slope = 0.0076 ºC·yr-1, F1,107 = 2.820, p = 0.092). Red line represents the fitted linear regression model. Data source IPCC Data Distribution Centre.

1880 1900 1920 1940 1960 1980 2000

Year

-4

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ano

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C)

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Fig. S2. Graphical representation of the autocorrelation function at different temporal lags (from 1 to 15 years) for the arrival time series of each one of the Slovakian stork population percentiles (from 5% to 95%). Data for the period 1895-1913 and 1977-2007 are shown separately. Significance of correlations was tested by the Box-Ljung Q statistic. Dotted lines denote the statistical significance range at p = 0.05 (it is different for each study period because of the different sample size in each case). Evidence for temporal autocorrelation was not found in any time-series.

Time lag (years)

Au

toco

rre

latio

n

3 6 9 12 15

-0.45

0.00

0.45

3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

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3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

5%

30%

55%

80% 85% 90% 95%

60% 65% 70% 75%

35% 40% 45% 50%

10% 15% 20% 25%

1895-1913

Time lag (years)

Au

toco

rre

latio

n

3 6 9 12 15

-0.35

0.00

0.35

3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

3 6 9 12 15

-0.35

0.00

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3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

3 6 9 12 15

-0.35

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3 6 9 12 15 3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

3 6 9 12 15

-0.35

0.00

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3 6 9 12 15 3 6 9 12 15 3 6 9 12 15

5%

30%

55%

80% 85% 90% 95%

60% 65% 70% 75%

35% 40% 45% 50%

10% 15% 20% 25%

1977-2007

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Fig. S3. Differences in the average arrival date of each percentile of the Slovakian male population of white storks between the periods 1895-1913 and 1977-2007. Error bars denote the 95% confidence interval.

5 15 25 35 45 55 65 75 85 95

Percentile (%)

80

85

90

95

100

105

110

115

Ave

rage

arr

ival

dat

e

1895-1913 1977-2007

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Fig. S4. Relationship between the arrival date and the breeding success of individuals each year. Arrival dates were calculated as differences with the average arrival of the population of each sex each year. The red lines represent the fitted regression lines.

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20072006200520042003

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dglin

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Arrival date-40 -20 0 20 40 -40 -20 0 20 40 -40 -20 0 20 40 -40 -20 0 20 40 -40 -20 0 20 40

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1990 1991 1992 1993 1995

1984 1985 1986 1987 1988

1978 1979 1980 1981 1982

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Arrival date-40 -20 0 20 40 -40 -20 0 20 40 -40 -20 0 20 40

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Females

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1990 1991 1992 1993 1995

1984 1985 1986 1987 1988

1978 1979 1980 1981 1982

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gs

Arrival date-40 -20 0 20 40 -40 -20 0 20 40 -40 -20 0 20 40

-40 -20 0 20 40

Females

-40 -20 0 20 40 -40 -20 0 20 40

Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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Fig. S5. Variation of the breeding success in males (a) and females (b) in a two-dimensional space defined by the arrival date and year. Lines define areas of equal breeding success. Darker areas represent higher breeding success. Areas were drawn using a negative exponential smoother.

.

1980 1985 1990 1995 2000 2005

Year

20-Mar

30-Mar

9-Apr

19-Apr

29-Apr

Arr

ival

dat

e

(a)

1980 1985 1990 1995 2000 2005

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30-Mar

9-Apr

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29-Apr

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Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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Fig. S6. Predicted map of male arrivals during the period 1895-1913. Values predicted by the PLSR model (see Table 1) have been mapped only for the eastern sector of Slovakia (>19.6ºE longitude) because there were available data only for that part of the country. See scale colour bar at the bottom of the figure for correspondences between colours and dates.

Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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Fig. S7. Moran's I autocorrelograms for residuals from partial least square regression models. Each mark in the x-axis represents an interval of 30 km of distance. Maximum distance varied for each model because of the different extent of records in each case (see Fig. 6).

-0.15

-0.10

-0.05

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Mo

ran

's I

Male 1895-1913

Male 1977-2007

Female 1977-2007

Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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Table S1. Slovakian localities with records of arrival dates of stork males during both periods. The median values obtained from the sample of each period were used in a paired t-test to look for differences in arrival dates between periods. The number of days of difference between the median arrival dates of the period 1895-1913 and 1977-2007 are shown in the last column. Positive values mean later dates, while negative values earlier dates.

Site Latitude (ºN) Longitude (ºE) Median arrival

date 1895-1913 Median arrival

date 1977-2007 Difference Bardejov 49.29 21.27 87 94 7 Drienovec 48.60 20.95 95 91 -4 Humenne 48.93 21.91 92.5 91 -1.5 Janik 48.55 20.97 84 85 1 Lemesany 48.85 21.27 100.5 87 -13.5 Lubica 49.12 20.44 99 87 -12 Moldava nad Bodvou 48.61 21.00 97 98.5 1.5 Petrovce n/Laborcom 49.00 21.48 99 118 19 Podolinec 49.25 20.53 97 93.5 -3.5 Presov 49.00 21.24 90 94 4 Revuca 48.68 20.11 95 86.5 -8.5 Rimavska Sobota 48.38 20.02 92 96 4 Roznava 48.66 20.52 96 92 -4 Sarisske Michalany 49.07 21.13 83 93 10 Snina 48.99 22.15 107 85 -22 Spisska Bela 49.19 20.45 93 94 1 Stara Lubovna 49.30 20.69 96 111 15 Svidnik 49.30 21.57 96 85 -11 Tibava 48.74 22.21 99 84 -15 Tovarne 48.91 21.75 88 91.5 3.5 Velke Ozorovce 48.66 21.61 95 90 -5 Vysny Lanec 48.52 21.12 86 97 11 Zborov 49.37 21.30 94 91.5 -2.5

Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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Table S2. Results of the simple regression models between the median arrival date to Slovakia and temperatures in the selected weather stations of the wintering and passage areas of Africa and Europe. Each weather station is identified by its World Meteorological Organization code (WMO-id). Its coordinates, country and months used are shown in each one. The correlation coefficient (r), the regression coefficient (slope) and the significance (p) of the regressions for the median arrival of males in the period 1895-1913, males in the period 1977-2003, and females in the period 1977-2003 are shown. P-values below 0.05 are in bold. Slopes values are mapped in the Fig. 3.

WMO-id latitude longitude Country Period Males 1895-1913 Males 1977-2003 Females 1977-2003

r slope p r slope p r slope p

12772 48.10 20.78 Hungary Mar-Apr -0.2975 -0.7288 0.1318 -0.3496 -1.1724 0.0739

12840 47.52 19.03 Hungary Mar-Apr -0.1865 -0.7037 0.4446 -0.3468 -0.8582 0.0764 -0.3468 -1.1800 0.0764

12843 47.43 19.18 Hungary Mar-Apr -0.3696 -0.9308 0.0578 -0.3709 -1.2715 0.0568

12882 47.48 21.63 Hungary Mar-Apr -0.1615 -0.4931 0.5089 -0.3576 -0.9083 0.0671 -0.4371 -1.4592 0.0226

12942 46.00 18.23 Hungary Mar-Apr -0.4567 -1.0004 0.0166 -0.4083 -1.2334 0.0345

12982 46.25 20.10 Hungary Mar-Apr -0.4633 -1.0711 0.0150 -0.4844 -1.4977 0.0105

15085 47.13 24.50 Romania Mar-Apr -0.3326 -0.7531 0.0901 -0.4279 -1.2695 0.0260

15120 46.78 23.57 Romania Mar-Apr -0.1530 -0.4780 0.5318 -0.3423 -0.6192 0.0805 -0.3543 -0.8747 0.0698

15247 45.77 21.25 Romania Mar-Apr -0.0586 -0.1878 0.8117 -0.3984 -0.8960 0.0396 -0.4614 -1.3695 0.0154

15260 45.80 24.15 Romania Mar-Apr -0.0988 -0.2788 0.6874 -0.3591 -0.7458 0.0658 -0.4545 -1.2252 0.0172

15360 45.15 29.67 Romania Mar-Apr 0.2506 0.6580 0.3008

15420 44.40 26.10 Romania Mar-Apr 0.0668 0.1635 0.7858 -0.3436 -0.6054 0.0793 -0.3770 -0.9014 0.0526

15552 43.82 23.25 Bulgaria Mar-Apr -0.4518 -0.8983 0.0180 -0.4646 -1.2635 0.0146

15614 43.20 27.92 Bulgaria Mar-Apr -0.4519 -0.8563 0.0180 -0.5056 -1.2530 0.0071

15511 42.65 23.38 Bulgaria Mar-Apr -0.4798 -0.9033 0.0113 -0.4723 -1.2221 0.0129

15655 42.48 27.48 Bulgaria Mar-Apr -0.4383 -0.8753 0.0222 -0.4474 -1.2248 0.0193

13274 44.80 20.47 Serbia Mar-Apr -0.0499 -0.1504 0.8392 -0.4261 -0.8406 0.0267 -0.4347 -1.1473 0.0235

17022 41.20 32.30 Turkey Mar-Apr -0.5324 -1.0200 0.0043 -0.5699 -1.4680 0.0019

17050 41.67 26.57 Turkey Mar-Apr -0.5243 -1.0932 0.0050 -0.5688 -1.5819 0.0020

17062 40.97 29.08 Turkey Mar-Apr -0.5822 -1.1956 0.0014 -0.6148 -1.6983 0.0006

17112 40.13 26.40 Turkey Mar-Apr -0.4943 -1.0931 0.0088 -0.5685 -1.6849 0.0020

17116 40.18 29.07 Turkey Mar-Apr -0.4254 -0.6286 0.0270 -0.4048 -0.8397 0.0362

Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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17130 40.00 32.90 Turkey Mar-Apr -0.4565 -0.8736 0.0167 -0.4849 -1.2246 0.0104

17188 38.67 29.42 Turkey Mar-Apr -0.3620 -0.6968 0.0635 -0.4180 -1.0950 0.0300

17190 38.75 30.53 Turkey Mar-Apr -0.3865 -0.7588 0.0464 -0.4954 -1.2512 0.0086

17240 37.75 30.55 Turkey Mar-Apr -0.3163 -0.6928 0.1080 -0.4463 -1.2497 0.0196

17292 37.20 28.35 Turkey Mar-Apr -0.2957 -0.6734 0.1343 -0.4272 -1.2380 0.0262

17300 36.80 31.40 Turkey Mar-Apr -0.3401 -0.8746 0.0826 -0.3494 -1.2390 0.0740

40007 36.18 37.22 Syria Mar-Apr -0.3722 -0.9332 0.0559 -0.4468 -1.4466 0.0195

40022 35.53 35.77 Syria Mar-Apr -0.3838 -1.4918 0.0481 -0.5029 -2.4806 0.0075

40030 35.13 36.72 Syria Mar-Apr 0.0080 0.0390 0.9741 -0.3464 -0.8327 0.0767 -0.4240 -1.3694 0.0275

40061 34.55 38.30 Syria Mar-Apr -0.3276 -0.8637 0.0953 -0.4398 -1.5018 0.0217

40080 33.42 36.52 Syria Mar-Apr -0.2971 -0.7506 0.1323 -0.3779 -1.2584 0.0520

40180 32.00 34.90 Israel Mar-Apr -0.2683 -0.7644 0.1760 -0.2730 -1.0788 0.1683

40199 29.55 34.95 Israel Mar-Apr -0.3336 -1.0936 0.0890 -0.3611 -1.5898 0.0642

40250 32.50 38.20 Jordan Mar-Apr -0.3533 -1.0273 0.0706 -0.4572 -1.7361 0.0165

40310 30.17 35.78 Jordan Mar-Apr -0.3458 -0.9222 0.0773 -0.3685 -1.3349 0.0586

62366 30.13 31.40 Egypt Mar-Apr 0.1753 1.1319 0.4729 -0.3666 -1.2060 0.0600 -0.3943 -1.7496 0.0418

62315 31.20 29.90 Egypt Mar-Apr -0.0620 -0.2902 0.8009

62414 24.03 32.88 Egypt Mar-Apr -0.2750 -0.6900 0.1651 -0.2666 -0.9313 0.1789

62440 30.60 32.25 Egypt Mar-Apr 0.1336 0.8554 0.5856

62600 21.92 31.32 Sudan Feb-Mar 0.0146 0.0351 0.9424 -0.1721 -0.5756 0.3907

62640 19.53 33.32 Sudan Feb-Mar 0.0191 0.0376 0.9247 -0.2524 -0.6734 0.2040

62650 19.17 30.48 Sudan Feb-Mar 0.0046 0.0088 0.9818 -0.1876 -0.4958 0.3487

62721 15.60 32.55 Sudan Feb-Mar 0.0532 0.1105 0.7921 -0.1566 -0.4472 0.4354

62730 15.47 36.40 Sudan Feb-Mar 0.1594 0.2909 0.4271 0.0653 0.1682 0.7462

62752 14.03 35.40 Sudan Feb-Mar 0.1451 0.2884 0.4702 -0.0368 -0.1029 0.8554

62771 13.17 30.23 Sudan Feb-Mar 0.1729 0.3218 0.3885 -0.0101 -0.0263 0.9601

62805 11.78 34.38 Sudan Feb-Mar 0.1967 0.7049 0.3254 0.1380 0.6914 0.4924

63686 0.53 35.28 Kenya Jan-Feb 0.1722 0.7998 0.3904 0.2403 1.5747 0.2273

63740 -1.32 36.92 Kenya Jan-Feb 0.2128 0.8207 0.2866 0.2672 1.4489 0.1779

63832 -5.08 32.83 Tanzania Jan-Feb 0.1260 0.8789 0.5312 0.3083 3.0155 0.1177

63962 -10.68 35.58 Tanzania Jan-Feb 0.1529 0.8805 0.4464 0.2136 1.7338 0.2847

67475 -10.22 31.13 Zambia Jan-Feb 0.0386 0.1699 0.8484 0.1930 1.1673 0.3348

Gordo et al. Complex phenological changes and their consequences in the breeding success of a migratory bird, the white stork Ciconia ciconia

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67663 -14.45 28.47 Zambia Jan-Feb 0.2369 1.3925 0.2341 0.2604 2.1228 0.1896

67743 -17.82 25.82 Zambia Jan-Feb 0.1600 0.5103 0.4253 0.1445 0.6501 0.4721

67775 -17.92 31.13 Zimbabwe Jan-Feb 0.1806 1.2862 0.4594 0.2432 1.1154 0.2216 0.1496 0.9594 0.4564

67964 -20.15 28.62 Zimbabwe Jan-Feb 0.2311 1.1488 0.3411 0.3942 1.1328 0.0419 0.2863 1.1384 0.1477

68174 -23.87 29.45 South Africa Jan-Feb 0.4149 1.4593 0.0314 0.3628 1.7939 0.0629

68262 -25.73 28.18 South Africa Jan-Feb 0.2691 0.8721 0.1747 0.2659 1.2159 0.1801

68368 -26.13 28.23 South Africa Jan-Feb 0.5251 1.4308 0.0049 0.4614 1.7580 0.0154