Climate and spatio-temporal variation in the population dynamics of a long distance migrant, the white stork

Journal of Animal Ecology

2006

75

80ndash90

copy 2006 British Ecological Society

Blackwell Publishing Ltd

Climate and spatio-temporal variation in the population dynamics of a long distance migrant the white stork

BERNT-ERIK SAEligTHER VIDAR GROslashTAN PIOTR TRYJANOWSKIdagger CHRISTOPHE BARBRAUDDaggersect STEINAR ENGENpara and MIROSLAV FULIN

Department of Biology Norwegian University of Science and Technology N-7491 Trondheim Norway

dagger

Department of Behavioural Ecology Adam Mickiewicz University Umultowska 89 PL-61614 Pozna

n

Poland

Dagger

Centre drsquoEtudes Biologiques de Chizeacute CNRS-UPR 1934 79360 Villiers en Bois France

sect

Groupe Ornithologique Aunis Saintonge Palais des Congregraves 17300 Rochefort France

para

Department of Mathematical Sciences Norwegian University of Science and Technology N-7491 Trondheim Norway and

East-Slovakian Museum Kosice Hviezdoslavova 3 SK-041 36 Ko

szlig

ice Slovakia

Summary

1

A central question in ecology is to separate the relative contribution of densitydependence and stochastic influences to annual fluctuations in population size Here weestimate the deterministic and stochastic components of the dynamics of differentEuropean populations of white stork

Ciconia ciconia

We then examined whetherannual changes in population size was related to the climate during the breeding period(the lsquotap hypothesisrsquo

sensu

Saeligther Sutherland amp Engen (2004

Advances in EcologicalResearch

35

185ndash209) or during the nonbreeding period especially in the winter areasin Africa (the lsquotube hypothesisrsquo)

2

A general characteristic of the population dynamics of this long-distance migrant issmall environmental stochasticity and strong density regulation around the carryingcapacity with short return times to equilibrium

3

Annual changes in the size of the eastern European populations were correlated byrainfall in the wintering areas in Africa as well as local weather in the breeding areasjust before arrival and in the later part of the breeding season and regional climatevariation (North Atlantic Oscillation) This indicates that weather influences the popu-lation fluctuations of white storks through losses of sexually mature individuals as wellas through an effect on the number of individuals that manages to establish themselvesin the breeding population Thus both the tap and tube hypothesis explains climateinfluences on white stork population dynamics

4

The spatial scale of environmental noise after accounting for the local dynamics was67 km suggesting that the strong density dependence reduces the synchronizing effectsof climate variation on the population dynamics of white stork

5

Several climate variables reduced the synchrony of the residual variation in popula-tion size after accounting for density dependence and demographic stochasticityindicating that these climate variables had a synchronizing effect on the populationfluctuations In contrast other climatic variables acted as desynchronizing agents

6

Our results illustrate that evaluating the effects of common environmental variableson the spatio-temporal variation in population dynamics require estimates and model-ling of their influence on the local dynamics

Key-words

climate effects on population dynamics density dependence environmentalstochasticity population synchrony white stork


(2006)

75

80ndash90doi 101111j1365-26562005001023x

Correspondence Bernt-Erik Saeligther Department of Biology Norwegian University of Science and Technology N-7491 Trond-heim Norway E-mail bernteriksatherbiontnuno

81

Variation in stork population dynamics



75

80ndash90

Introduction

It is has been known for a long time (Lack 1966 Lawton1996) especially from studies of passerines (CurnuttPimm amp Maurer 1996 Saeligther

et al

2003) and gamebirds (Cattadori amp Hudson 1999 Williams Ives ampApplegate 2003) that variation in population dynamicsoccurs within the distribution range of bird speciesHowever the mechanisms behind these patterns arepoorly understood (Brown Mehlman amp Stevens 1995Lawton 1996) We propose that such regional variationin population dynamics can arise in two different waysFirst the influence of variation in environmental variableson population dynamics may differ geographically Forinstance the influence of a large-scale climate phe-nomenon the North Atlantic Oscillation (NAO) onfluctuations in the size of great tit

Parus major

and piedflycatcher

Ficedula hypoleuca

populations differ overshort distances as well as shows gradients over largergeographical scales (Saeligther

et al

2003) Secondlyspatial variation in the deterministic components ofpopulation dynamics such as the strength of the den-sity dependence may also induce regional patterns inpopulation dynamics Accordingly assuming a logisticmodel of density regulation large variation was foundamong populations of the great tit and the pied fly-catcher in the specific population growth rate

r

(Saeligther

et al

2003) which in this model determines the rate ofreturn to equilibrium or the strength of density depend-ence (May 1981) Thus an evaluation of these twohypotheses for intraspecific variation in populationdynamics requires separate estimates of the parametersspecifying the deterministic component of the dynamicsas well as stochastic influences on local populationfluctuations Unfortunately several studies of birds(Saeligther

et al

1998 2000 Saeligther amp Engen 2002) haveshown that obtaining reliable estimates of those para-meters even after neglecting the influence of age structurerequires precise long-term population counts that areavailable only for a few species

Comparative studies have shown that environmentalstochasticity has a strong influence on the populationdynamics of birds (Saeligther

et al

2004a 2005) Such sto-chastic fluctuations in the environment can influencefluctuations in population size in two different waysAs suggested by Lack (1966) environmental variationduring the breeding season can affect the number ofrecruits produced resulting in a change in populationsize the next breeding season This was termed the lsquotaphypothesisrsquo by Saeligther Sutherland amp Engen (2004b)Alternatively according to the lsquotube hypothesisrsquo changesin population size from one year to the next may beaffected by the number of birds that manage to surviveduring the nonbreeding season which will be influencedby the environmental fluctuations during this time ofthe year The relative contribution of the tube- and taphypothesis for the effects of environmental stochasticityon the variability of bird populations is however poorlyunderstood (Saeligther

et al

2004b)

In 1953 Moran published a highly influential paperstating that common environmental noise in isolatedpopulations described by the same linear model willinduce a correlation in fluctuating population sizesequal to the correlation in local environmental noiseHowever Lande Engen amp Saeligther (1999) showed usinga homogeneous continuous model that the correlationbetween population sizes will increase by increasingmigration A clear prediction that appeared from theseanalyses was that the effect of migration on the scale ofpopulation synchrony decreased with increasing densityregulation Thus whether regional variation in popu-lation dynamics will result in large-scale synchrony inpopulation fluctuations will depend on the deterministiccomponents as well as the stochastic influences on thepopulation dynamics

The white stork

Ciconia ciconia

lives in close relation-ship with humans in agricultural areas In combinationwith a conspicuous breeding habit (Creutz 1985) thismakes it possible to obtain relatively precise populationestimates Population sizes of white storks declinedduring the nineteenth century over larger parts of itsbreeding areas in western Europe (Rheinwald Ogdenamp Schulz 1989) however this decrease seems now to bereversed in many countries (Barbraud Barbraud ampBarbraud 1999 Schulz 1999 Doligez 2004 SchaubPradel amp Lebreton 2004) Such declines have howeverrarely been recorded in eastern Europe probably dueto less intensified agricultural practices

Here we will estimate the strength of density depend-ence and the stochastic influences on different whitestork populations using methods described in LandeEngen amp Saeligther (2003) We will then assess how differentlocal and regional climate variables such as the NAO(Hurrell 1995) affect local population fluctuations ofwhite storks in Poland and Slovakia where most indi-viduals winter in Central or Southern Africa (Creutz1985) The white stork is suitable for such analysesbecause adult survival is correlated by temporal vari-ation in rainfall in the wintering areas (Kanyamibwa

et al

1990 Kanyamibwa Bairlein amp Schierer 1993Schaub Kania amp Koumlppen 2005)

Because the femalesusually do not start breeding before they are 3 years orolder (Bairlein amp Zink 1979 Creutz 1985 Bairlein1991) we can examine whether climate variation in thewintering areas can affect annual variation in popula-tion change as expected from the lsquotube hypothesisrsquo of(Saeligther

et al

2004b) Finally following Engen

et al

(inpress) we will estimate the spatial synchrony in popu-lation fluctuations and how environmental variablesthat affect local population dynamics influence the spa-tial scale of that synchrony

Materials and methods

The study sites are located (Fig 1) throughout the Republicof Poland the Slovak Republic and the Czech Republic

82

B-E Saeligther

et al



75

80ndash90

(Hladik 1989) In addition we used individual-baseddemographic data from a population in Charente-Maritime in western France that was re-established in1978 (Barbraud

et al

1999) Because the French birdshave different wintering areas than the birds from thestudy areas in eastern Europe (Creutz 1985 Barbraudunpublished data) we did not include the populationin France within our comparative analyses

Data on individual variation in fitness was only avail-able from the population in western France in which alarge proportion of the nestlings were ringed each year(Barbraud

et al

1999) Because of their conspicuousbreeding habit by extensive use of artificial nesting sitesa large proportion of all individuals breeding in the areacould be checked for rings by use of spotting scopes

The white stork builds large perennial nests that aremost commonly located close to human settlementsand therefore are relatively easy to find and to observeduring the breeding period (eg Creutz 1985) The sizeof the local populations was estimated by standardmethods used during the International Census of WhiteStorks (Creutz 1985) For further details on methodssee Tryjanowski amp Kuzniak (2002) Ptaszyk

et al

(2003)Tryjanowski Sparks amp Profus (2005a) and Tryjanowski

et al

(2005b)

Two different population models were used For thosepopulations in which there was no significant negativerelationship between relative changes in populationsize

N

from year

t

to

t

+ 1

∆

N

N

on

N

we assumed thatpopulation sizes were kept so far below

K

that densityregulation was impossible to estimate Hence a populationmodel without density regulation was adopted so that

E

(

∆

N

|

N

t

)

=

rN

t

eqn 1a

and

eqn 1b

where

r

is the specific population growth rate is thedemographic variance and is the environmental vari-ance The first order approximation of the mean andvariance in

∆

X

=

X

t

+

1

minus

X

t

= ln

N

t

+1

minus

ln

N

t

is then

eqn 2a

and

eqn 2b

In those cases in which density regulation was presentwe fitted the theta-logistic model of density regulation(Gilpin amp Ayala 1973) We assume that the logarithm ofchange in population size

∆

X

=

ln(

N

+

∆

Ν

) minus

ln

(

Ν

)

takes the form

ln

λ

(

N

) =

reg

[1

minus

(

NK

)

θ

] (Saeligther

et al

2000) eqn 3

where is the population growthrate in the absence of stochasticity

K

is the carryingcapacity

reg

the mean specific growth rate at

N

= 0 and

θ

describes the form of density regulation FollowingSaeligther

et al

(2002a) eqn 3 may alternatively be writtenas where

r

1

=

reg

(1

minus

K

minusθ

)is the specific growth rate when

N

= 1 At

N

=

K

with

λ

(

K

) = 1 the strength of density dependence is

γ

(

K

) =

reg

θ

(Saeligther

et al

2000) Thus strong densitydependence and short return times to equilibriumat

K

(May 1981) occurs when the specific populationgrowth rate is high andor for large values of

θ

Wealso see that when

θ

= 0 (Gompertz density regulation)

γ

(

K

) =

r

1

ln

K

and when

θ

= 1 (logistic density regula-tion)

γ

(K) = reg (Saeligther et al 2002a Saeligther Engen ampMatthysen 2002b) The moments of the stationary distribu-tion of population size N for the theta-logistic model are

eqn 4

where and Γ denotes the gammafunction (Diserud amp Engen 2000)

To examine the effects of climate on the populationfluctuations we rewrite our population models (eqns 1and 3) on the form

eqn 5

where E denotes the expectation Ud and Ue are inde-pendent variables with zero mean and unit varianceand no temporal autocorrelation We can use eqn 5 toexamine how different climate variables affect fluctu-ations in population size by modelling climate variable yi

as random effect (Saeligther et al 2004b) writing

Fig 1 Location of the study populations in eastern EuropeFor location of the French population see Barbraud et al(1999)

var( ) N N N Nt t d t e t+ = +12 2 2| σ σ

σd2

σe2

E X X r et e

Xd

t( ) ∆ | = minus minus minus12

12

2 2σ σ

var( ) ∆X X et dX

et| = +minusσ σ2 2

λ( ) ( )N N N N= + ∆

ln ( ( )( ))λ θ θ= minus minus minusr N K1 1 1 1

EN

Km

mm

m

m

=

+

+

=Γ

Γ

αθ

αθ

αθ

θ1

1 2

α σ θ ( ) ( )= minus minus2 112r Ke

X E X X U N Ut t t d d t e e+ += + +1 1 ( ) | σ σ

83Variation in stork population dynamics

copy 2006 British Ecological Society Journal of Animal Ecology 75 80ndash90

eqn 6

where U is another standardized variable βi is theregression coefficient for the effects of climate variablenumber i and σ2 is the component of the environmen-tal variance that cannot be explained by fluctuations inthe covariates This leads to the relation

eqn 7

so that the covariates together explain a fraction

eqn 8

of the total environmental variance in the noiseSeveral climate variables were included in the analyses

The NAO is a regional climate phenomenon that refersto variation in sea-level pressure differences between theArctic and subtropical Atlantic (Hurrel 1995 Hurrellet al 2003) We used the NAO index for the winter(DecemberndashMarch) period that is based on the differenceof normalized sea-level pressure between Lisbon Por-tugal and StykkisholmurReykjavik Iceland (httpwwwcgducareducasjhurrell naostatwinterhtml)We also used monthly means of temperature and pre-cipitation at local weather stations (obtained from theNational Oceanic and Atmospheric Administration athttpwwwnoaagov) for the period FebruaryndashSeptemberto describe the weather in the breeding areas To char-acterize the weather in the wintering areas we computedthe monthly standardized Sahel rainfall index from 14stations located between latitudes 8degSminus20degN and lon-gitudes 20degWminus10degE obtained from httpjisaowash-ingtonedudatasahel In addition we also includedprecipitation anomalies for Africa (httplwfncdc noaagovoaclimateresearchghcnghcngrid_prcphtml)which was computed for each square in a 5 times 5 degree gridwithin the wintering areas of white storks from easternEurope for the period NovemberndashFebruary (Creutz1985 Berthold et al 2002 2001ab)

Following Engen et al (2005a) the analyses of spatialsynchrony in population dynamics were based on stud-ying the residuals obtained from fitting the populationmodels (eqn 1 or eqn 3) to time-series observations inlocation z

eqn 9

We used the normal approximation and chose a para-metric form for the spatial autocorrelation of the U

eqn 10

where h(z) decreases from 1 to 0 as z increases from 0 toinfinity One likely positive definite autocorrelationfunction is the exponential form h(z) = endashz l Here weapplied following Lande et al (1999) the standarddeviation l of the scaled form of this function as a measure

of spatial scaling defined for the residuals Simulationstudies have shown that this procedure gives robustestimators for the spatial synchrony of population fluc-tuations (Lillegaringrd Engen amp Saeligther 2005)

Individual-based demographic data were only availablefor the population in western France Following Engenet al 2005b we calculated the demographic variance

from the projection matrix (Caswell 2001) based onthe contributions (Bit Iit) for the different age-classes iin year t where Bit is the number of offspring producedof a female of age i in year t and Iit = 1 if a mother ofage i survives between year t and t + 1 or Iit = 0 if shedies We separated these contributions into componentsthat are generated by demographic stochasticity ineach vital rate

For those populations in which we assumed expon-ential population growth (eqn 2) our estimates arethose derived from the likelihood function obtained byassuming that Xt+1 given Xt is normally distributedWriting xt for the observed log abundances in year t thelog likelihood function

eqn 11

where The likelihood function for thestochastic growth rate s = r minus 12νt was maximizednumerically with respect to the two unknown parametersr and

For those populations in which there was densitydependence we estimated the parameters in the theta-logistic model (eqn 3) by means of least square techniques(see Saeligther et al 2000 and Saeligther et al 2002a forprocedures)

Unfortunately reliable estimates of r1 are difficult toobtain and are often also biased because it is often nec-essary to interpolate the population fluctuations overlarge ranges of nonobserved values of N (Aanes et al2002) In this study an estimate of r1 was only obtainedfor the population in western France that was followedfrom re-establishment up to reaching carrying capacity(Barbraud et al 1999) This estimate was used whenestimating θ and for the density-regulated popula-tions in eastern Europe

To reduce the number of parameters we assumed alogistic model (θ = 1) for the density-regulated popu-lations when estimating the effects of climatic covari-ates and spatial synchrony in the fluctuations of easternEuropean populations Following Engen et al (2005a)the complete likelihood function for the spatial scalingof the residual variation after accounting for densitydependence demographic stochasticity and differentclimate variables at each locality defined by eqns 9 and

U y Ue e i i tσ β σ = sum +

σ β σe i i ty2 2 var( ) = sum +

ψ β β σ var( )[var( ) ] = sum sum +i i t i i ty y 2

R z X z E X z X z Y

z U z z U z N zt t t t t

t d d t t

( ) ( ) [ ( ) | ( ) ]

( ) ( ) ( ) ( ) ( )

= minus

asymp ++ +1 1

σ σ

ρ ρ ρ ρ( ) [ ( ) ( )] ( ) ( )z U w U w z h z= + = + minusinfin infincorr 0

σd2

ln ( ) ln

L r

x x r

e t

t t t

tt

n

σ νν

ν2

1

2

1

112

12

= minus +minus + minus

+

=

minus

sum

ν σ σt e dxe t = + minus2 2

σe2

σe2

84B-E Saeligther et al


10 was maximized numerically to give estimates forρ0ρinfin and l The sampling properties of the estimates arefound by parametric bootstrapping (Efron amp Tibshirani1993) The residuals are simulated from the appropriatemultinormal model defined by the autocorrelationfunction and the distance matrix The multinormallikelihood function can be calculated numerically usinga lower triangular linear transformation the Choleskidecomposition (Riply 1987) which can also be used togive the stochastic simulations required for performingthe bootstrapping (see Engen et al 2005a and Lillegaringrdet al 2005) The significance of a change in the esti-mates due to inclusion of covariates was estimated byexamining whether 0 was included in the appropriatelower and upper quantiles of the distribution for thedifferences between the two bootstrap distributions(Efron amp Tibshirani 1993)

Results

The pattern in the annual fluctuations in the popula-tion size differed among the white stork populations(Fig 2) The trajectory of the French populationwas characterized by an establishment period followedby some years with rapid growth During the recentyears the population has fluctuated around someequilibrium size The populations in eastern Europein which density dependence seems to be present werecharacterized by relatively small annual fluctuations(Fig 2)

The stochastic components of the white stork popu-lation in western France was = 0middot098 and =0middot035 The specific growth rate at N = 1 was r1 = 0middot189An extremely strong density regulation occurred aroundK ( = 11middot52) although this estimate was uncertain

Fig 2 Annual fluctuations in the size of the study populations For locations of the eastern European populations see Fig 1

d2

e2



with bootstrap replicates almost uniformly distributedover the interval 4 lt θ lt 100

The mean of the estimates of θ in the density-regulatedpopulations in eastern Europe (Fig 1) was = 2middot76(Table 1) ranging from 1middot18 to 4middot05 This shows thatmaximum density regulation in white stork populationsoccurs close to K Accordingly the mean time for returnto equilibrium 1γ was short (2middot52 years) indicatingstrong density dependence A consequence of this is thatthe presence of density dependence in the populationdynamics will be difficult to identify if the populationsize is below K

For populations with positive growth rates andassuming exponential growth the estimates of the spe-cific population growth rate ranged from 0middot0107 to0middot0539 (Fig 2 Table 1) with a mean annual populationgrowth rate of 2middot84

The influence of environmental stochasticity on whitestork population dynamics in eastern Europe was smallwith a mean environmental variance of 0middot0075(Table 1) For instance in two populations with nodensity-dependent effects (CHY and POZ) and in onepopulation with strong density dependence (SNW) was very close to zero No significant (P gt 0middot1) differencewas found between the two types of density-dependentmodels in the mean values of

As a consequence of a combination of strong densitydependence and small environmental stochasticity thevariance of the quasi-stationary distribution of N (eqn 4) was small This shows that a characteristic ofwhite stork population dynamics is small fluctuationsaround K (Fig 2 Table 1)

After accounting for the effects of density depend-ence variation in climate at the breeding sites as well asin the wintering areas influenced the residual variation inpopulation size in several of the populations Changes inpopulation size were positively related to winter NAOin 13 of the 17 populations Assuming that the signs ofthe regression coefficient are binomially distributed withprobability p = 0middot5 if there are no systematic climaticinfluences there was a higher number of positive β thanexpected just by chance [P = 0middot049] β gt 0 was signi-ficant (P lt 0middot05) in seven populations (BJ OB POPPOZ RS SU and TAT) NAO strongly affects localwinter weather over large areas of the northern hemi-sphere (Hurrel 1995 Hurrell et al 2003) Accordinglyin 15 of the populations [P = 0middot0023] a positive regres-sion coefficient β (see eqn 6) was found for temperatureduring February [β gt 0 significant (P lt 0middot05) in thelocalities POP RS RU and SNW] In 12 of 17 populationsβ was larger than 0 [P = 0middot144] also for precipitationduring this month [although β gt 0 significant (P lt 0middot05)in only the localities RS and TAT] ie relatively largepopulations were found after mild and wet FebruariesFurthermore the weather during the final stage of thebreeding season affected the population size the fol-lowing years For instance the population change fromt to t + 1 was positively related to the mean temperatureduring June and July in year t in 15 of 17 populations[β gt 0 significant (P lt 0middot05) in the localities CZ POPRS and SU] Similarly temperature during MayndashJuneexplained the highest average proportion of the varia-tion in population size for any of the weather variablesduring the breeding period p = 0middot23 β gt 0 significant

Table 1 The estimates of the parameters for each population (for locations see Fig 1) either assuming a theta-logistic model ofdensity regulation (eqn 3) or the exponential growth model (eqn 1) The figures in the brackets denote the 95 confidence intervalWe assume a demographic variance = 0middot098 and for the populations in which a theta-logistic model was fitted a constant specificgrowth rate at N = 1 r1 = 0middot188 θ is the form of density regulation r is the deterministic specific growth rate for the exponentialgrowth model K is the carrying capacity the environmental variance γ is the strength of density regulation at K and CV is thecoefficient of variation in the quasi-stationary distribution σNK with initial population size K

Locality r K θ γ CV

Exponential growthBJ 0middot042 [0middot020 0middot064] 0middot00782 [0middot00255 0middot01381]CHY 0middot033 [0middot014 0middot054] 0middot00131 [0middot00000 0middot00556]CZ 0middot011 [minus0middot007 0middot029] 0middot00437 [0middot00064 0middot00827]KL minus0middot007 [minus0middot028 0middot016] 0middot00530 [0middot00061 0middot01079]POZ 0middot000 [minus0middot011 0middot011] 0middot00000 [0middot00000 0middot00116]SA 0middot018 [minus0middot011 0middot049] 0middot01479 [0middot00212 0middot02825]SL 0middot013 [minus0middot010 0middot040] 0middot00922 [0middot00225 0middot01778]TAT 0middot054 [0middot030 0middot082] 0middot01055 [0middot00348 0middot01847]ZY minus0middot033 [minus0middot061 minus0middot004] 0middot00787 [0middot00042 0middot01711]Theta-logistic density regulationLS 54 [51ndash57] 3middot22 [1middot87ndash5middot55] 0middot61 0middot00725 [0middot00290ndash0middot01310] 0middot09OB 55 [53ndash57] 3middot93 [2middot27ndash6middot60] 0middot74 0middot00278 [0middot00030ndash0middot00650] 0middot06POP 13 [10ndash15] 1middot58 [0middot59ndash4middot20] 0middot30 0middot02055 [0middot00641ndash0middot03920] 0middot22RS 57 [54ndash61] 3middot01 [1middot65ndash5middot91] 0middot57 0middot00499 [0middot00150ndash0middot00980] 0middot08RU 17 [15ndash19] 2middot56 [1middot35ndash5middot29] 0middot48 0middot01450 [0middot00430ndash0middot02850] 0middot15SK 19 [17ndash21] 2middot51 [1middot41ndash5middot15] 0middot47 0middot00853 [0middot00171ndash0middot00800] 0middot12SNW 14 [14ndash15] 4middot05 [2middot57ndash6middot40] 0middot76 0middot00000 0middot07SU 28 [15ndash23] 1middot18 [0middot27ndash4middot84] 0middot23 0middot00528 0middot15

σd2

σe2

σe2

σe2

e2

σe2

σN2



(P lt 0middot05) in the localities BJ CZ POP RU SK andSNW] All together a significant effect of temperaturefor some interval during the period MayndashJuly wasfound in 52 of the populations

Weather in the wintering areas of the white stork inAfrica also influenced the population dynamics In 14of 17 [P = 0middot013] populations changes in populationsize was positively related to the index for rainfall inthe Sahel region during January or February [althoughβ gt 0 was significant (P lt 0middot05) in only two of thelocalities (SK and ZY)] Similarly population changeswere also correlated to Sahel rainfall during October in13 populations [P = 0middot049] in which β was significantly(P lt 0middot05) larger than 0 in 4 populations (LS POP RSand SU) However the largest average effect was foundfor the Sahel rainfall during December (2 = 0middot26) Insix populations (BJ LS OB POP RS and RU) this wasrelated to a significant (P lt 0middot05) negative effect of rain-fall on fluctuations in population size

There was also large temporal and spatial variationwithin the wintering areas in the autocorrelation betweenrainfall and annual changes in population size (Fig 3)Using gridded (5 times 5 degrees) anomalies (see Methods)we found positive effects of rainfall during November

and February in Sudan and Ethiopia (Fig 2ad) Fur-thermore rainfall in Kenya and in eastern Tanzaniaduring the period NovemberndashJanuary also has a positiveeffect on the growth rates of most populations (Fig 3andashc) In contrast rainfall in Zambia Botswana and SouthAfrica especially during November (Fig 3a) was relatedto a decrease in population size Finally rainfall inMozambique in the period DecemberndashFebruary alsoaffected the population fluctuations of the white stork(Fig 3bndashd) with a negative effect of rainfall duringDecember and February but with a positive relationshipbetween change in population size and rainfall duringJanuary

Thus these analyses show that population fluctu-ations of the white stork were explained by weather atdifferent parts of the year Consequently seasonalvariation was also found in the relative contribution oftemperature and precipitation to the environmentalstochasticity Of the climatic variables in the breedingareas temperature in MayndashJune the preceding year(2 = 0middot23) summer (JunendashAugust) precipitation (2 =0middot22) and temperature during February (2 = 0middot19)explained on average the highest proportion of thevariance in This was similar to the average proportion

Fig 3 The influence of variation in rainfall during November (a) December (b) January (c) and February (d) in different partsof Africa on the fluctuations in the size of eastern European white stork populations Grids in which β gt 0 in 12 or morepopulations are indicated with red colour whereas grids in which β lt 0 in 12 or more populations are indicated with green Thegrey areas denote grids included in the analyses

σe2



explained by regional climate phenomena such the NAO(2 = 0middot19) and Sahel rainfall (2 = 0middot23 and 2 = 0middot21for October and March respectively) However this isa slightly smaller proportion than explained by rainfallanomalies in 5 times 5 degrees grids in the wintering areasin Africa In fact for the grid located in Mozambique(Fig 3) 2 = 0middot28 for rainfall in February for the grid atthe border area between Tanzania and Mozambique2 = 0middot26 for rainfall during December and for the gridat the border between Tanzania and Zambia 2 = 0middot26for rainfall during January

After accounting for the local effects of densitydependence and demographic stochasticity the spatialcorrelation in the residual variation in population sizedecreased with distance (Fig 4) The spatial scale wasl = 67 km that was significantly (P lt 0middot05) larger than0 The correlation at zero distance ()0 = 0middot518) was notsignificantly different from 1 (P gt 0middot1) whereas thecorrelation in the noise at infinite distance ()infin = 0middot214)was not significantly different from 0 (P gt 0middot1)

Weather affected the spatial synchrony of the popu-lation fluctuations (Fig 4) After accounting for theeffects on the local dynamics of regional weather phe-nomena such as the NAO (Fig 4a) or Sahel rainfall(Fig 4c) the spatial correlation in the residual variationin population fluctuations at given distance generallydecreased showing that these regional climate variablessynchronized the population dynamics of white storksin eastern Europe A similar effect was also found for

temperature during MayndashJune in year t minus 1 at the breed-ing grounds (Fig 4b) In contrast weather in some partsof the wintering areas (Fig 4d) generally increased theresidual variation in population sizes This shows thatclimate variation may also act desynchronizing on thepopulation fluctuations of white storks

Discussion

This study shows that a characteristic of white storkpopulation dynamics is strong density dependence(Table 1 Fig 2) and relatively small environmentalstochasticity (Table 1) that are influenced by climaticconditions during the breeding season as well as in thewintering areas in Africa (Fig 3) Local climate in thebreeding areas acted mainly by synchronizing the spa-tial variation in residual population fluctuations afteraccounting for density dependence and demographicstochasticity in the local dynamics (Fig 4)

These analyses are based on several simplifyingassumptions1 Obtaining unbiased estimates of the specific growthrate r1 are extremely difficult for populations fluctuat-ing around the carrying capacity (Aanes et al 2002Lande et al 2002) We therefore assumed that the spe-cific growth rate r1 for the population in western Francealso was typical for all our eastern European populationsAlthough this estimate lies within the range that is estimatedfor several other bird species it is considerably higher

Fig 4 The effects on spatial synchrony in residual size of eastern European white stork populations after accounting fordemographic stochasticity and density dependence of including (a) NAO (b) local temperature during May and June during thebreeding season in year t minus 1 (c) Sahel rainfall during December and (d) precipitation anomalies in the grid 25ndash30degS 20ndash25degE(see Fig 3) during February The solid line is the estimate based on including no climatic covariate The dotted line shows theestimated spatial autocorrelation of residual variation in population size after including the covariate in the local models Thickline denotes the 50 quantile and thin lines the 2middot5 and 97middot5 quantile respectively



than the estimates of r from the populations in whichthere was no evidence for density dependence (Fig 2Table 1) A biased r will affect the estimates of θ througha negative sampling covariance and often results in veryuncertain estimates of θ (Saeligther et al 2000) Hence ifwe have overestimated r1 our estimates of θ (Table 1)are too small indicating even stronger density regulationaround K However the estimates of γ are less influencedby biases in r1 (Engen et al unpublished data)2 No data were available from any eastern Europeanpopulation on individual variation in fitness that arenecessary for obtaining estimates of demographicvariance Thus we used the estimate = 0middot098 of theFrench population that lay within the lower range ofvariation recorded for species with similar life-historycharacteristics of the white stork (Saeligther et al 2004a)If we have underestimated our estimates of (Table 1) are likely to represent overestimates (EngenSaeligther amp Moslashller 2001)3 In our analyses we have ignored age-structure effectsthat are known (Caswell 2001 Lande et al 2002) to induceautocorrelations in time series of population fluctua-tions in such long-lived species such as the white stork(Kanyamibwa et al 1993 1990) Thus our estimates of (Table 1) also include a component that is due to fluctua-tions in the age structure and thus represent an over-estimate of the effects of environmental stochasticity onpopulation dynamics Because our estimates (Table 1)of θ or γ are larger (Saeligther et al 2000 2002a Lande et al2002 Saeligther amp Engen 2002) and our estimates of are smaller (Saeligther et al 2000 2004a) than in many otherbird populations this only supports the conclusion thatpopulation dynamics of the white stork are characterizedby strong density regulation with relatively small envi-ronmentally induced fluctuations around K

The strong density regulation recorded in white storkpopulations may be related to their social organizationWhite storks often defend a territory during the breed-ing season that is used for foraging (Creutz 1985) Thusat high densities access to suitable breeding territorieswith either sufficient food or suitable breeding sites maybe limited Such a regulation of populations throughterritorial behaviour is common in birds (Newton 1998)and has previously been suggested to occur also in whitestorks (Tryjanowski amp Kuzniak 2002) In fact in theFrench population the number of interactions betweenbreeding pairs increased with increasing populationsize particularly at sites with the highest densities Theseinteractions ranged from displays in flight to fights andcould eventually result in destruction of clutches or smallchicks (Barbraud unpublished data) Alternativelydensity regulation may also operate through limitedavailability of food resources at the wintering groundsin Africa (Bairlein 1991) or at the breeding groundsAccordingly in many altricial bird species densitydependence primarily operates during the nonbreedingseason (Saeligther et al 2004b)

Several studies have shown that the demography ofthe white stork is influenced by weather both in the

wintering areas (Kanyamibwa et al 1990 1993) and atthe breeding grounds (Zink 1967 Jovani amp Tella 2004)This study demonstrates that these climate-mediatedchanges in demography affect annual variation in sizeof most of the populations mainly due to a combinedeffect of local summer and winter weather as well as therainfall at the winter grounds (Fig 3) Large popula-tion sizes were found after large rainfall especially ineastern Africa (Fig 3andashc) probably reflecting highersurvival in those years (Kanyamibwa et al 1990 1993Schaub et al 2005) A combination of satellite teleme-try studies and ringing recoveries have shown thatthose areas are important wintering areas for easternEuropean white storks (Berthold et al 2001a 2001b)Furthermore large effects on the change in populationsize were found in Sudan Ethiopia and Kenya for rainfallduring DecemberndashJanuary (Fig 3andashc) which coin-cides with a period of high accumulation of body massafter the end of autumn migration However rainfallparticularly in southern Africa could also have negativeeffects on population fluctuations of most white storkpopulations in eastern Europe (Fig 3) Such spatialheterogeneity in the effects of the same climate variableon the population dynamics have also been recorded inother bird species as well (Saeligther et al 2003 2004b)

Because most white storks do not start breedingbefore they are 3 or 4 years of age (Bairlein amp Zink 1979Creutz 1985 Bairlein 1991) the effects on summerweather on next yearrsquos population size cannot be directlyrelated to variation in fledgling production This suggeststhat the influence of summer climate on populationdynamics operates through a climate-mediated effect onthe cost of reproduction Alternatively summer weathermay also affect the gain of body resources among thenonbreeding birds that in turn affect their survival ortheir probability of obtaining a territory the followingspring Accordingly large seasonal variation is foundin the body mass of white storks (Berthold et al 2001a)Finally there was also a tendency for larger populationsizes following high temperatures in February Thiseffect was surprising because most white storks arrivedat our study sites from the end of March (Ptaszyk et al2003 Tryjanowski et al 2004) Because arrival occurslater and breeding success is poorer in cold years (Zink1967 Tryjanowski et al 2004) we suggest that Februarytemperatures affect the phenological development andthat more new recruits will be able to establish them-selves as breeders in years with a warm February Onereason for this may be that warm pre-breeding seasonsincrease resource availability or improve foraging effi-ciency for the white storks Whatever mechanisms ourresults indicate that the population dynamics of whitestorks in eastern Europe are likely to be sensitive tochanges in climate both in Africa and Europe

Climate-induced changes in population size of smallpasserine temperate birds often occur through an effectof weather during the nonbreeding season and thussupport the lsquotube hypothesisrsquo of Saeligther et al (2004b)Such an effect was also present in white stork population

σd2

d2

σd2

σe2

σe2

σe2



dynamics (Table 1) In addition some evidence sug-gests that summer weather may affect recruitment thefollowing breeding season as expected from a lsquotap effectrsquoof climate Such an effect of recruitment driven changesin population size seems to be typical for the populationdynamics of many precocial birds

Although local fluctuations of eastern Europeanwhite stork populations were influenced by variation inclimate variables that were correlated over large areasthe spatial scaling of the residual variation in popula-tion size after accounting for density dependence wasfar shorter (Fig 4) than recorded in small temperatepasserines (Saeligther et al in prep) and for the continentalgreat cormorant Phalacrocorax carbo sinensis in centralEurope (Engen et al 2005a) One reason for this dif-ference is the strong density dependence in thepopulation dynamics of the white stork (Table 1) andcontinental great cormorant (Engen et al 2005a) thatis expected to decrease the spatial scaling of the syn-chrony in population fluctuations (Lande et al 1999)However climate variation was still able to affect thesynchronizing of the population dynamics in easternEurope (Fig 4) Most climate variables acted by syn-chronizing the population dynamics (Fig 4andashc) Adesynchronizing effect of weather in southern Africawas also present (Fig 4d) This is in accordance withtheoretical results showing that large spatial heteroge-neity in the effects of environmental variables on localdynamics can reduce the spatial synchrony even thoughthe environmental variable is autocorrelated over largeareas (Engen amp Saeligther 2005) However all these effectswere far from significant

Acknowledgements

We are grateful to the large number of people thathelped with the field work in eastern Europe especiallyZ Jakubiec L Jerzak S Kuzniak P Profus J PtaszykA Wuczynski and J Kosicki Furthermore we thank themembers of the Groupe Ornithologique Aunis Saintongeespecially Jean-Claude and Monique Barbraud Jeanand Liliane Biron for their lifelong commitment tothe conservation of white stork Karine Delord andDominique Besson and the CRBPO that supervisedthe white stork ringing scheme in France This studywas financed by a grant from the Research Council ofNorway (NORKLIMA)

References

Aanes S Engen S Saeligther B-E Willebrand T ampMarcstroumlm V (2002) Sustainable harvesting strategies ofWillow Ptarmigan in a fluctuating environment EcologicalApplications 12 281ndash290

Bairlein F (1991) Population studies of White Storks(Ciconia ciconia) in Europe In Bird Population Studies (edsC Perrins J-D Lebreton amp GJM Hirons) pp 207ndash229Oxford University Press Oxford

Bairlein F amp Zink G (1979) Der Bestand des WeissstorchsCiconia ciconia Suumldwestdeutschland eine Analyse derBestandsentwicklung Journal fuumlr Ornithologie 120 1ndash11

Barbraud C Barbraud JC amp Barbraud M (1999) Popu-lation dynamics of the White Stork Ciconia ciconia in west-ern France Ibis 141 469ndash479

Berthold P van den Bossche W Fiedler W Gorney EKaatz M Lesheim Y Nowak E amp Querner U (2001a)Der Zug des Weissstorchs (Ciconia ciconia) eine besondereZugform auf Grund neuer Ergebnisse Journal fuumlr Ornithologie142 73ndash92

Berthold P van den Bossche W Fiedler W Kaatz MLesheim Y Nowak E amp Querner U (2001b) Detection ofa new important staging and wintering area of the White StorkCiconia ciconia by satellite tracking Ibis 143 450ndash455

Berthold P Bossche WVD Jakubiec Z Kaatz C KaatzM amp Querner U (2002) Long-term satellite tracking shedslight upon variable migration strategies of White Storks(Ciconia ciconia) Journal fuumlr Ornithologie 143 489ndash493

Brown JH Mehlman DW amp Stevens GE (1995) Spatialvariation in abundance Ecology 76 2028ndash2043

Caswell H (2001) Matrix Population Models SinauerSunderland MA

Cattadori IM amp Hudson PJ (1999) Temporal dynamics ofgrouse populations at the southern edge of their distribu-tion Ecography 22 374ndash383

Creutz G (1985) Der Weiss-Storch A Ziemsen VerlagWittenberg Lutherstadt

Curnutt JL Pimm SL amp Maurer BA (1996) Populationvariability of sparrows in space and time Oikos 76 131ndash144

Diserud O amp Engen S (2000) A general and dynamic speciesabundance model embracing the lognormal and the gammamodels American Naturalist 155 497ndash511

Doligez B Thomson DL amp van Noordwijk AJ (2004)Using large-scale data analysis to assess life history andbehavioural traits the case of the reintroduced White StorkCiconia ciconia population in the Netherlands AnimalBiodiversity and Conservation 27 387ndash402

Efron B amp Tibshirani RJ (1993) An Introduction to theBootstrap Chapman amp Hall New York

Engen S amp Saeligther B-E (2005) Generalizations of theMoran effect explaining spatial synchrony in populationfluctuations American Naturalist 166 603ndash612

Engen S Saeligther B-E amp Moslashller AP (2001) Stochasticpopulation dynamics and time to extinction of a decliningpopulation of barn swallows Journal of Animal Ecology70 789ndash797

Engen S Lande R Saeligther B-E amp Weimerskirch H (2005b)Extinction in relation to demographic and environmentalstochasticity in age-structured models Mathematical Bio-sciences 195 210ndash227

Engen S Lande R Saeligther B-E amp Bregnballe T (2005a)Estimating the synchrony of fluctuating populations Jour-nal of Animal Ecology 74 601ndash611

Gilpin ME amp Ayala FJ (1973) Globals models of growthand competition Proceedings of the National Academy ofSciences USA 70 3590ndash3593

Hladik B (1989) Bestandsaumlnderungen des Weissstorchs imnordosten des Boumlhmisch-Maumlhrischen Huumlgellandes InWhite Stork Status and Conservation (eds G Rheinwald JOgden amp H Schulz) pp 77ndash80 Dachverband DeutscherAvifaunisten Braunschweig

Hurrell JW (1995) Decadal trends in the North-AtlanticOscillation-regional temperatures and precipitation Science269 676ndash679

Hurrell JW Kushnir Y Ottersen G amp Visbeck M (2003)An overview of the North Atlantic Oscillation In The NorthAtlantic Oscillation Climate Significance and EnvironmentalImpact (eds JW Hurrell Y Kushnir G Ottersen amp MVisbeck) pp 1ndash35 American Geophysical UnionWashington DC

Jovani R amp Tella JL (2004) Age-related environmentalsensitivity and weather mediated nestling mortality in whitestorks Ciconia ciconia Ecography 27 611ndash618



Kanyamibwa S Schierer A Pradel R amp Lebreton JD(1990) Changes in adult annual survival rates in a westernEuropean population of the White Stork Ciconia ciconiaIbis 132 27ndash35

Kanyamibwa S Bairlein F amp Schierer A (1993) Compar-ison of survival rates between populations of White StorkCiconia ciconia Central Europe Ornis Scandinavica 24297ndash302

Lack D (1966) Population Studies of Birds Oxford UniversityPress Oxford

Lande R Engen S amp Saeligther B-E (1999) Spatial scale ofpopulation synchrony environmental correlation versusdispersal and density regulation American Naturalist 154271ndash281

Lande R Saeligther B-E Engen S Filli F Matthysen E ampWeimerskirch H (2002) Estimating density dependencefrom population time series using demographic theory andlife-history data American Naturalist 159 321ndash332

Lande R Engen S amp Saeligther B-E (2003) Stochastic Popula-tion Dynamics in Ecology and Conservation Oxford Univer-sity Press Oxford

Lawton JH (1996) Population abundances geographic rangesand conservation 1994 Witherby Lecture Bird Study 433ndash19

Lillegaringrd M Engen S amp Saeligther BE (2005) Bootstrapmethods for estimating spatial synchrony of fluctuatingpopulations Oikos 109 342ndash350

May RM (1981) Models for single populations InTheoretical Ecology (ed RM May) pp 5ndash29 BlackwellScientific Publications Oxford

Moran PAP (1953) The statistical analysis of the Canadianlynx cycle II Synchronization and meteorology AustralianJournal of Zoology 1 291ndash298

Newton I (1998) Population Limitation in Birds AcademicPress San Diego

Ptaszyk J Kosicki J Sparks TH amp Tryjanowski P (2003)Changes in arrival pattern of the White Stork Ciconiaciconia in western Poland Journal fuumlr Ornithologie 144323ndash329

Rheinwald G Ogden J amp Schulz H (1989) White StorkConservation and Status Rheinischer Landwirtschafts-VerlagBonn

Riply B (1987) Stochastic Simulation John Wiley and SonsNew York

Saeligther B-E amp Engen S (2002) Pattern of variation in avianpopulation growth rates Philosophical Transactions of theRoyal Society B 357 1185ndash1195

Saeligther B-E Engen S Islam A McCleery R amp Perrins C(1998) Environmental stochasticity and extinction risk in apopulation of a small songbird the great tit AmericanNaturalist 151 441ndash450

Saeligther B-E Engen S Lande R Arcese P amp SmithJNM (2000) Estimating the time to extinction in an islandpopulation of song sparrows Proceedings of the RoyalSociety London B 267 621ndash626

Saeligther B-E Engen S amp Matthysen E (2002a) Demo-graphic characteristics and population dynamical patternsof solitary birds Science 295 2070ndash2073

Saeligther BE Engen S Lande R Visser M amp Both C(2002b) Density dependence and stochastic variation in a

newly established population of a small songbird Oikos99 331ndash337

Saeligther BE Engen S Moslashller AP Matthysen EAdriansen F Fiedler W Leivits A Lambrechts MMVisser ME Anker-Nilssen T Both C Dhondt AAMcCleery RH McMeeking J Potti J Roslashstad OW ampThomson D (2003) Climate variation and regional gradi-ents in population dynamics of two hole-nesting passerinesProceedings of the Royal Society London B 270 2397ndash2404

Saeligther BE Engen S Moslashller AP Weimerskirch HVisser ME Fiedler W Matthysen E Lambrechts MMBadyaev A Becker PH Brommer JE Bukacinski DBukacinska M Christensen H Dickinson J du Feu CGehlbach F Heg D Houmltker H Merilauml J Nielsen JTRendell W Robertson RJ Thomson DL Toumlroumlk J ampVan Hecke P (2004a) Life-history variation predicts theeffects of demographic stochasticity on avian populationdynamics American Naturalist 164 793ndash802

Saeligther BE Sutherland WJ amp Engen S (2004b) Climateinfluences on a population dynamics Advances in EcologicalResearch 35 185ndash209

Saeligther B-E Engen S Moslashller AP Visser ME MatthysenE Fiedler W Lambrechts MM Becker PH BrommerJE Dickinson J Gehlbach F Merilauml J Rendell WRobertson RJ Thomson D amp Toumlroumlk J (2005) Time toextinction of bird populations Ecology 86 693ndash700

Schaub M Pradel R amp Lebreton J-D (2004) Is the reintro-duced white stork (Ciconia ciconia) population in Switzerlandself-sustainable Biological Conservation 119 105ndash114

Schaub M Kania W amp Koumlppen U (2005) Variation ofprimary production during winter induces synchrony insurvival rates in migratory white storks Ciconia ciconiaJournal of Animal Ecology 74 656ndash666

Schulz (1999) White stork on the up Proceedings of the Inter-national Symposium on the White Stork Hamburg 1996Naturschutzbund Deutschland Bonn

Tryjanowski P amp Kuzniak S (2002) Size and productivity of theWhite Stork Ciconia ciconia population in relation to CommonVole Microtus arvalis density Ardea 90 213ndash217

Tryjanowski P Sparks TH Ptaszyk J amp Kosicki J (2004)Do White storks Ciconia ciconia profit from an early returnto their breeding grounds Bird Study 52 222ndash227

Tryjanowski P Sparks T amp Profus P (2005a) Uphill shiftsin the distribution of the white stork Ciconia ciconia insouthern Poland the importance of nest quality Diversityand Distributions 11 219ndash223

Tryjanowski P Sparks TH Jakubiec Z Jerzak LKosicki J Kuzniak S Profus P Ptaszyk J amp WuczynskiA (2005b) The relationship between population means andvariance in reproductive success differs between localpopulations of White Stork (Ciconia ciconia) PopulationEcology 47 119ndash125

Williams CK Ives AR amp Applegate RD (2003) Populationdynamics across geographical ranges time-series analysesof three small game species Ecology 84 2654ndash2667

Zink G (1967) Populationsdynamik des Weissen StorchsCiconia ciconia in Mitteleuropa Proceedings of the Inter-national Ornithological Congresses 14 191ndash215

Received 13 June 2005 accepted 22 July 2005

81

Variation in stork population dynamics



75

80ndash90

Introduction

It is has been known for a long time (Lack 1966 Lawton1996) especially from studies of passerines (CurnuttPimm amp Maurer 1996 Saeligther

et al

2003) and gamebirds (Cattadori amp Hudson 1999 Williams Ives ampApplegate 2003) that variation in population dynamicsoccurs within the distribution range of bird speciesHowever the mechanisms behind these patterns arepoorly understood (Brown Mehlman amp Stevens 1995Lawton 1996) We propose that such regional variationin population dynamics can arise in two different waysFirst the influence of variation in environmental variableson population dynamics may differ geographically Forinstance the influence of a large-scale climate phe-nomenon the North Atlantic Oscillation (NAO) onfluctuations in the size of great tit

Parus major

and piedflycatcher

Ficedula hypoleuca

populations differ overshort distances as well as shows gradients over largergeographical scales (Saeligther

et al

2003) Secondlyspatial variation in the deterministic components ofpopulation dynamics such as the strength of the den-sity dependence may also induce regional patterns inpopulation dynamics Accordingly assuming a logisticmodel of density regulation large variation was foundamong populations of the great tit and the pied fly-catcher in the specific population growth rate

r

(Saeligther

et al

2003) which in this model determines the rate ofreturn to equilibrium or the strength of density depend-ence (May 1981) Thus an evaluation of these twohypotheses for intraspecific variation in populationdynamics requires separate estimates of the parametersspecifying the deterministic component of the dynamicsas well as stochastic influences on local populationfluctuations Unfortunately several studies of birds(Saeligther

et al

1998 2000 Saeligther amp Engen 2002) haveshown that obtaining reliable estimates of those para-meters even after neglecting the influence of age structurerequires precise long-term population counts that areavailable only for a few species

Comparative studies have shown that environmentalstochasticity has a strong influence on the populationdynamics of birds (Saeligther

et al

2004a 2005) Such sto-chastic fluctuations in the environment can influencefluctuations in population size in two different waysAs suggested by Lack (1966) environmental variationduring the breeding season can affect the number ofrecruits produced resulting in a change in populationsize the next breeding season This was termed the lsquotaphypothesisrsquo by Saeligther Sutherland amp Engen (2004b)Alternatively according to the lsquotube hypothesisrsquo changesin population size from one year to the next may beaffected by the number of birds that manage to surviveduring the nonbreeding season which will be influencedby the environmental fluctuations during this time ofthe year The relative contribution of the tube- and taphypothesis for the effects of environmental stochasticityon the variability of bird populations is however poorlyunderstood (Saeligther

et al

2004b)

In 1953 Moran published a highly influential paperstating that common environmental noise in isolatedpopulations described by the same linear model willinduce a correlation in fluctuating population sizesequal to the correlation in local environmental noiseHowever Lande Engen amp Saeligther (1999) showed usinga homogeneous continuous model that the correlationbetween population sizes will increase by increasingmigration A clear prediction that appeared from theseanalyses was that the effect of migration on the scale ofpopulation synchrony decreased with increasing densityregulation Thus whether regional variation in popu-lation dynamics will result in large-scale synchrony inpopulation fluctuations will depend on the deterministiccomponents as well as the stochastic influences on thepopulation dynamics

The white stork

Ciconia ciconia

lives in close relation-ship with humans in agricultural areas In combinationwith a conspicuous breeding habit (Creutz 1985) thismakes it possible to obtain relatively precise populationestimates Population sizes of white storks declinedduring the nineteenth century over larger parts of itsbreeding areas in western Europe (Rheinwald Ogdenamp Schulz 1989) however this decrease seems now to bereversed in many countries (Barbraud Barbraud ampBarbraud 1999 Schulz 1999 Doligez 2004 SchaubPradel amp Lebreton 2004) Such declines have howeverrarely been recorded in eastern Europe probably dueto less intensified agricultural practices

Here we will estimate the strength of density depend-ence and the stochastic influences on different whitestork populations using methods described in LandeEngen amp Saeligther (2003) We will then assess how differentlocal and regional climate variables such as the NAO(Hurrell 1995) affect local population fluctuations ofwhite storks in Poland and Slovakia where most indi-viduals winter in Central or Southern Africa (Creutz1985) The white stork is suitable for such analysesbecause adult survival is correlated by temporal vari-ation in rainfall in the wintering areas (Kanyamibwa

et al

1990 Kanyamibwa Bairlein amp Schierer 1993Schaub Kania amp Koumlppen 2005)

Because the femalesusually do not start breeding before they are 3 years orolder (Bairlein amp Zink 1979 Creutz 1985 Bairlein1991) we can examine whether climate variation in thewintering areas can affect annual variation in popula-tion change as expected from the lsquotube hypothesisrsquo of(Saeligther

et al

2004b) Finally following Engen

et al

(inpress) we will estimate the spatial synchrony in popu-lation fluctuations and how environmental variablesthat affect local population dynamics influence the spa-tial scale of that synchrony

Materials and methods

The study sites are located (Fig 1) throughout the Republicof Poland the Slovak Republic and the Czech Republic

82

B-E Saeligther

et al



75

80ndash90


et al



et al



et al


et al

(2005b)


N

from year

t

to

t

+ 1

∆

N

N

on

N


K


E

(

∆

N

|

N

t

)

=

rN

t

eqn 1a

and

eqn 1b

where

r


∆

X

=

X

t

+

1

minus

X

t

= ln

N

t

+1

minus

ln

N

t

is then

eqn 2a

and

eqn 2b


∆

X

=

ln(

N

+

∆

Ν

) minus

ln

(

Ν

)

takes the form

ln

λ

(

N

) =

reg

[1

minus

(

NK

)

θ

] (Saeligther

et al

2000) eqn 3


K


reg


N

= 0 and

θ


et al


r

1

=

reg

(1

minus

K

minusθ


N

= 1 At

N

=

K

with

λ

(

K


γ

(

K

) =

reg

θ

(Saeligther

et al


K


θ


θ


γ

(

K

) =

r

1

ln

K

and when

θ


γ


eqn 4



eqn 5





σd2

σe2

E X X r et e

Xd


12

2 2σ σ

var( ) ∆X X et dX


λ( ) ( )N N N N= + ∆


EN

Km

mm

m

m

=

+

+

=Γ

Γ

αθ

αθ

αθ

θ1

1 2





eqn 6


eqn 7


eqn 8




eqn 9


eqn 10






eqn 11








R z X z E X z X z Y


t d d t t

( ) ( ) [ ( ) | ( ) ]

( ) ( ) ( ) ( ) ( )

= minus

asymp ++ +1 1

σ σ


σd2

ln ( ) ln

L r

x x r

e t

t t t

tt

n

σ νν

ν2

1

2

1

112

12


+

=

minus

sum


σe2

σe2




Results




d2

e2












σd2

σe2

σe2

σe2

e2

σe2

σN2









σe2







Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References





























































82

B-E Saeligther

et al



75

80ndash90


et al



et al



et al


et al

(2005b)


N

from year

t

to

t

+ 1

∆

N

N

on

N


K


E

(

∆

N

|

N

t

)

=

rN

t

eqn 1a

and

eqn 1b

where

r


∆

X

=

X

t

+

1

minus

X

t

= ln

N

t

+1

minus

ln

N

t

is then

eqn 2a

and

eqn 2b


∆

X

=

ln(

N

+

∆

Ν

) minus

ln

(

Ν

)

takes the form

ln

λ

(

N

) =

reg

[1

minus

(

NK

)

θ

] (Saeligther

et al

2000) eqn 3


K


reg


N

= 0 and

θ


et al


r

1

=

reg

(1

minus

K

minusθ


N

= 1 At

N

=

K

with

λ

(

K


γ

(

K

) =

reg

θ

(Saeligther

et al


K


θ


θ


γ

(

K

) =

r

1

ln

K

and when

θ


γ


eqn 4



eqn 5





σd2

σe2

E X X r et e

Xd


12

2 2σ σ

var( ) ∆X X et dX


λ( ) ( )N N N N= + ∆


EN

Km

mm

m

m

=

+

+

=Γ

Γ

αθ

αθ

αθ

θ1

1 2





eqn 6


eqn 7


eqn 8




eqn 9


eqn 10






eqn 11








R z X z E X z X z Y


t d d t t

( ) ( ) [ ( ) | ( ) ]

( ) ( ) ( ) ( ) ( )

= minus

asymp ++ +1 1

σ σ


σd2

ln ( ) ln

L r

x x r

e t

t t t

tt

n

σ νν

ν2

1

2

1

112

12


+

=

minus

sum


σe2

σe2




Results




d2

e2












σd2

σe2

σe2

σe2

e2

σe2

σN2









σe2







Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References































































eqn 6


eqn 7


eqn 8




eqn 9


eqn 10






eqn 11








R z X z E X z X z Y


t d d t t

( ) ( ) [ ( ) | ( ) ]

( ) ( ) ( ) ( ) ( )

= minus

asymp ++ +1 1

σ σ


σd2

ln ( ) ln

L r

x x r

e t

t t t

tt

n

σ νν

ν2

1

2

1

112

12


+

=

minus

sum


σe2

σe2




Results




d2

e2












σd2

σe2

σe2

σe2

e2

σe2

σN2









σe2







Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References
































































Results




d2

e2












σd2

σe2

σe2

σe2

e2

σe2

σN2









σe2







Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References








































































σd2

σe2

σe2

σe2

e2

σe2

σN2









σe2







Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References





































































σe2







Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References



































































Discussion












σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References





































































σd2

d2

σd2

σe2

σe2

σe2





Acknowledgements


References

































































Acknowledgements


References

































































































Climate and spatio-temporal variation in the population dynamics of a long distance migrant, the white stork

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