J Ornithol (2008) 149:199–210
DOI 10.1007/s10336-007-0260-z
ORIGINAL ARTICLE
The relative importance of conditions in wintering and passage
areas on spring arrival dates: the case of long-distance Iberian
migrants
Oscar Gordo Æ Juan José Sanz
Received: 10 April 2007 / Revised: 11 October 2007 / Accepted: 8 November 2007 / Published online: 27 November 2007
Ó Dt. Ornithologen-Gesellschaft e.V. 2007
Abstract Remote sensing data have been used in previous studies to assess the effects of winter ecological
conditions in Africa on biological parameters recorded in
bird populations during the following breeding season in
Europe. Based on the results of these studies, we hypothesized that a high productivity of vegetation during the
winter and, thus, high resource availability, should advance
the arrival of long-distance migrants to the European
breeding areas due to enhanced ecological conditions. To
test this hypothesis, between 1982 and 2000 we examined
the first arrival date to the Iberian Peninsula of five species
(White Stork, Cuckoo, Common Swift, Barn Swallow and
Nightingale) in relation to several explanatory variables:
ecological conditions in their African wintering grounds
and passage areas, as reflected by the normalized difference
vegetation index (NDVI), temperature and precipitation in
their passage areas and the winter North Atlantic Oscillation (NAO). Ecological conditions in the wintering areas
were important for White Stork, Cuckoo and Barn Swallow
phenology, while both NDVI in passage areas and NAO
did not have an effect on any species. Migratory birds
arrived earlier after winters with high vegetation productivity in Africa. Temperature in passage areas was
important for the later species (i.e. Cuckoo, Common Swift
Communicated by F. Bairlein.
Electronic supplementary material The online version of this
article (doi:10.1007/s10336-007-0260-z) contains supplementary
material, which is available to authorized users.
O. Gordo (&) J. J. Sanz
Departamento de Ecologı́a Evolutiva,
Museo Nacional de Ciencias Naturales (CSIC),
C/ José Gutiérrez Abascal 2, 28006 Madrid, Spain
e-mail: [email protected]
and Nightingale), although in all cases the true relevance of
this factor was scarce due to the poor explanatory capacity
of the models. These species were recorded in the Iberian
Peninsula earlier in the spring of those years with warmer
temperatures in passage areas. The nexus between African
NDVI and arrival phenology is hypothesized through
increases in wintering survival rates and/or the faster
acquisition of pre-migratory body condition and progression through sub-Saharan areas.
Keywords Africa Bird migration Climate Iberian Peninsula NDVI Phenology
Introduction
Trans-Saharan migratory birds spend part of their life cycle
south of the Sahara Desert (Moreau 1972; Curry-Lindahl
1981). Despite the time spent in these areas, the winter
ecology of these species is still rather poorly understood.
This problem is accentuated for most of the smaller bodied
birds because basic information on their wintering distribution is still scarce or non-existent (Walther and Rahbek
2002; Walther et al. 2004; Wisz et al. 2007). This gap in
our knowledge of species biology is of interest because
some species follow different ecological strategies during
their breeding season and wintering season, respectively
(Jones 1995; Marra and Holberton 1998; Baumann 2000;
Salewski et al. 2002a; Salewski and Jones 2006).
Environmental conditions during one part of the life
cycle may have significant consequences on subsequent
parts of the life cycle (Marra et al. 1998; Hogstad et al.
2003; Norris et al. 2004)—in some cases, even much later
(Thomson and Ollason 2001). In the case of long-distance
migrants, such as trans-Saharan bird species, wintering
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200
conditions have been shown to have detectable effects on
interannual variability in their population numbers (e.g.,
Svensson 1985). The survival of individuals can be affected by changes in resource availability, weather conditions,
interspecific competition, predation, and/or human habitat
destruction in their wintering and migratory areas (Winstanley et al. 1974; Mullié et al. 1995; Fasola et al. 2000;
van den Brink et al. 2000; Sillett and Holmes 2002;
Salewski et al. 2003; Bijlsma and van den Brink 2005;
Schaub et al. 2005).
Recently developed analytical techniques are now providing the means to indirectly explore the wintering
ecology of migratory birds (Szép and Møller 2004; Walther
et al. 2004; Pettorelli et al. 2005; Wisz et al. 2007). Satellites of the U.S. National Oceanic and Atmosphere
Administration (NOAA) have been measuring the dailyreflected radiation of the earth surface since 1981. This
collection of satellite data enables a value to be calculated
for the amount and vigour of vegetation on any land surface of the globe. This value is expressed as the normalized
difference vegetation index (NDVI), which relates reflected
wave-lengths to the level of photosynthetic activity
(Nicholson et al. 1998). Since most trans-Saharan migratory birds are insectivorous, and insect abundance depends
on plant productivity, the NDVI is likely to reflect the
abundance of insect supplies (Baumann 1999; Sanz et al.
2003; Szép et al. 2006).
Some recent studies have shown that winter NDVI in
Africa affects spring arrivals, sexual selection and reproductive success in a number of Italian and Danish
populations of barn swallows (Hirundo rustica; Møller
2004; Møller and Merilä 2004; Saino et al. 2004a, b;
Møller and Szép 2005; Szép et al. 2006). Saino et al.
(2004b) reported that unfavourable ecological conditions in
the sub-Saharan winter quarters of barn swallows breeding
in Italy resulted in delayed arrival only of individuals aged
2 years or older. In contrast, Møller and Merilä (2004)
reported that unfavourable ecological conditions in
Northern Africa resulted in earlier arrival dates of a population of barn swallows breeding in Denmark. The
conclusions drawn by these two studies seem to be contradictory, but two different areas in Africa (Western
Africa and Algeria) were used to study the relationship
between ecological conditions in Africa and the arrival to
the breeding colonies in Europe. Because these studies
focussed only on a single species (barn swallow) and on
two concrete European populations, the conclusions drawn
may not be applicable to the rest of the trans-Saharan birds
and/or populations, among which a great variety of ecological strategies, migratory periods and routes can be
found.
The aim of this study was to resolve this question using
five heterogeneous and common species of trans-Saharan
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J Ornithol (2008) 149:199–210
migratory birds. We examined whether the interannual
variability in their first arrival dates to the breeding grounds
was correlated to variations in environmental conditions
during the whole winter stay in sub-Saharan Africa, during
the month immediately prior to their departure from subSaharan Africa and during the migratory period over their
passage areas. Environmental conditions refer to the bulk
of biotic and abiotic factors that shape the characteristics of
environment at a certain moment. Ecological conditions
refer only to biotic factors (i.e. vegetation productivity,
food availability or the phenological development of ecosystems), while abiotic factors refer exclusively to the
physical features of the environment (i.e., weather). We
expected that years with high vegetation productivity (i.e.
high values of NDVI) in Africa would advance spring
arrivals since more favourable ecological conditions would
improve individuals’ survival, acquisition of pre-migratory
body condition and progression through sub-Saharan areas
as a result of higher food availability. As such, we also
expected that years with better ecological conditions in
passage areas would advance the first arrival dates due to
the improved foraging opportunities during stopovers.
Additionally, we hypothesized that the progression of
individuals will be faster and, therefore, the arrival date
would be earlier in those years with good weather conditions (i.e. high temperatures and low precipitation) during
the migration time in the passage areas.
Materials and methods
Bird phenological data
Spring migratory phenology of five trans-Saharan migrant
bird species was obtained for the period of 1982–2000
from the phenological database of the Spanish Instituto
Nacional de Meteorologı́a. This database is the result of a
phenological network set up by volunteer observers
throughout Spain (see for details Gordo and Sanz 2006).
The observers report the first sighted individual of the
White Stork Ciconia ciconia, Common Swift Apus apus
and Barn Swallow Hirundo rustica and the first singing
male of the Cuckoo Cuculus canorus and the Nightingale
Luscinia megarhynchos in their home towns and cities.
Records were averaged per year to obtain a single first
arrival date value in Spain for each species. One potential
difficulty in merging data into a single time-series for a
large area is the strong phenological differences in the
migratory calendar among populations (Gordo et al. 2007a,
b). For example, Barn Swallows arrive in southwestern
(SW) Spain around the end of February, whereas they
arrive at the beginning of May in some high-altitude
localities in central Spain (Gordo et al. 2007a; see Fig. 1c).
J Ornithol (2008) 149:199–210
201
These strong phenological differences in the migratory
calendar among populations can imply different environmental pressures acting on each population, which can be
difficult to assess if all data are combined in a single
measurement. Therefore, the separation of populations
with the earliest arrivals from those with the latest arrivals
may be appropriate since, using this approach, explanatory
variables defined for more precise periods within the
annual calendar can be attributed (see Table 1). Separation
between early and late populations was based on the geographical patterns of arrivals throughout Iberia. The
Cuckoo, the Common Swift and the Barn Swallow showed
well-established geographical patterns in terms of their
arrival dates (see Fig. 1; Gordo 2006; Gordo et al. 2007a)
and, consequently, we were able to easily divide up the
populations of these three species into early or late areas. In
fact, populations from SW Spain are notably earlier than
those from the rest of the country (see mean arrivals in
Table 1). The geographical patterns of the arrival dates of
the White Stork were not clear (Gordo et al. 2007b) and did
not allow an easy and objective division of Spain into an
early and late region, respectively. For the Nightingale, the
distribution of arrival dates was narrow (see low standard
deviation in Table 1), i.e. the differences between the
earliest and latest populations was small and, consequently,
this species did not appear to have strong geographical
gradients within Spain (Gordo 2006). For these reasons, we
studied the White Stork and the Nightingale within the
framework of a single time-series for the whole Spain. We
eventually obtained seven time-series of arrival dates (two
for the Cuckoo, Common Swift, and Barn Swallow, and
one for the White Stork and Nightingale).
Selection of explanatory variables: a hypothetical
framework for bird migratory phenology
Earlier studies have assessed the effect of environmental
changes—specifically climate changes (especially temperature)—on temporal variability in migratory phenology
(Gordo 2007). However, the effect of climate should be
hypothesized by assessing several intermediate mechanisms (see Fig. 2). Climate can act on migratory birds in
two main ways: directly, by means of weather conditions
during their migration (e.g. Ahola et al. 2004), and indirectly, through ecological conditions in those areas used
prior to the arrival site during both wintering and migration
(e.g. Saino et al. 2004b). These indirect effects can be
difficult to detect through the assessment of climatic variables (e.g. temperature) because in many cases we do not
know the key meteorological variable and/or time interval
on the annual calendar that determine those relevant ecological conditions for a particular species (Møller and
Fig. 1 Maps of first arrival dates for the Cuckoo (a), Common Swift
(b), and Barn Swallow (c) throughout Spain. The black line shows the
established boundary between early and late regions in each species.
Study localities are indicated as dots; black dots for early and white
dots for late localities. We have added a continuum surface of arrival
dates for the entire country created by the interpolation of arrival data
to enhance the visualization of the well-established geographical
patterns in arrival dates in these three species. The interpolation
procedure calculated an average value for each pixel from the six
nearest data points and weighted by the reciprocal squared distance
Merilä 2004; Stenseth and Mysterud 2005). Furthermore,
for most species and/or populations, researchers have only
a rough idea of the wintering and passage areas and, consequently, an imprecise determination of the spatial range,
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J Ornithol (2008) 149:199–210
Table 1 Range of months employed for calculations of NDVI and weather variables for each species
White Stork
Cuckoo
Common Swift
Barn Swallow
Nightingale
Early
Late
Early
Late
Early
Late
Mean of first
arrival dates
21 January
15 March
10 April
09 March
26 April
23 February
30 March
16 April
Standard
deviation
of first arrival
dates
28.75
14.50
14.82
22.60
17.93
17.62
20.31
16.15
NDVI during
winter
September–
December
October–
January
October–
February
October–
January
October–
March
October–
January
October–
February
October–
February
NDVI at
December–
departure time
January
February
February–
March
January–
February
March–
April
January–
February
February–
March
March
NW Morocco
January–
February
March
March–April
February–
March
April–May
February–
March
March–April
April
SW Iberia
January–
February
March–April
April
March–April
April–May
NDVI, Normalized difference vegetation index
NW Morocco and SW Iberia range were applied to NDVI, temperature and precipitation for these areas. Populations of the Cuckoo, Common
Swift and Barn Swallow were divided into early and late groups according to their phenologies (see Fig. 1). The mean first arrival date and its
standard deviation to Spain are also provided in each case
which makes it even more difficult to assess the temporal
effects of climatic variables. However, indirect effects of
climate can be easily assessed by means of NDVI measurements because these do quantify ecological conditions
and provide a fully comparable value among regions and
years (Pettorelli et al. 2005).
The NDVI is calculated as the normalized difference in
reflectance between the red (0.55–0.68 lm) and infrared
(0.73–1.1 lm) channels of the advanced, very high resolution radiometer (AVHRR) sensor of NOAA polar
orbiting satellites. The NDVI is ultimately determined by
the degree of absorption by chlorophyll in the red wavelengths, which is proportional to leaf chlorophyll density,
and by the degree of reflectance of near-infrared radiation,
Ecological
conditions
Food
availability
Body
condition
Moult
Climate
Winter
survival
Departure
from
wintering
grounds
Progression
speed
Population
size
Chances of
observation
ARRIVAL
DATE
Stopover
duration
Fig. 2 Diagram for the hypothetical framework proposed in this
study on potential variables that can influence arrival date phenology
of a bird migratory population. See text for more details
123
which is proportional to green leaf density (Tucker et al.
1985). Therefore, NDVI correlates well with such variables
as green leaf biomass, leaf area index, total dry matter
accumulation and annual net primary productivity (Nicholson et al. 1990). The NDVI data used in this study were
provided by Clark Labs as world monthly images at a
spatial resolution of 0.1° in scale values ranging from 0 to
255 covering the period from August 1981 to December
2000, with the exception of the period from September to
December 1994. For this reason, we employed first arrival
dates for the period 1982–2000, with the exception of 1995
(n = 18 years).
We gathered NDVI data for three regions: western
Africa, northwestern (NW) Morocco and SW Iberia (see
Fig. 3). Western Africa was considered to be the wintering
area, whereas NW Morocco and SW Iberia were considered to be passage areas. We focussed on western Africa
because both ringing recoveries (Fig. 3) and published
reports indicate that it is the main wintering area for the
Iberian populations of the species being studied here
(Cramp and Simmons 1977; Curry-Lindahl 1981; Cramp
1985, 1988; Urban et al. 1986; Fry et al. 1988; Keith et al.
1992). Results from previous studies have also demonstrated the importance of climate in this area for the spring
migratory phenology of the studied species (Dallinga and
Schoenmakers 1987; Gordo et al. 2005; Gordo and Sanz
2006). We focussed only on the SW quadrant of the Iberian
Peninsula because the earliest first arrival dates are recorded here, and this is the first region to be occupied during
the course of spring migration (Gordo 2006, Gordo et al.
2007a, b; see also Fig. 1).
J Ornithol (2008) 149:199–210
Fig. 3 Map showing selected grid cells (in grey) used for the
calculation of the normalized difference vegetation index (NDVI)
explanatory variables. Exact coordinates for all areas are indicated.
Dots recoveries of White Storks, squares recoveries of Barn Swallows
The selected areas include a great variety of habitats,
some or which are not used by the studied species to
overwinter (e.g. evergreen forests; Svensson 1985;
Salewski and Jones 2006) or are quickly crossed during
migration (e.g. deserts; Moreau 1961; Biebach et al. 2000).
IDRISI32 software for the Geographic Information System
(Clark Labs 2001) and the U.S. Geographical Survey
Global
Land
Use/Land
Cover
(available
at
http://edcdaac.usgs.gov/glcc/globe_int.html) were used to
exclude these little used areas because we expected that the
ecological conditions of these areas would have no effect
on migratory phenology. The inclusion of such non-used
environments add background noise to estimates of the true
ecological conditions found by migrants in their wintering
and passage areas and it may hinder the detection of
effects. The land cover layer used was created by the USGS
from NDVI measures of the AVHRR in 1992 and 1993 at
0.1° of resolution. Ideally, we should use a different layer
for each year to account for interannual transformations in
land uses. However, layers were not available for each
year. Since we used a land cover created in the middle of
our study period, we made the assumption that this layer
represents an average of the distributional changes that
occurred in the main types of land covers during our study
period. In western African, the excluded environments
(barren or sparsely vegetated, evergreen broadleaf forest,
wooded wetland and water bodies) represented 33.82% of
the grid cells, while in NW Morocco, the excluded
203
environments (barren or sparsely vegetated, evergreen
broadleaf and needleleaf forests and water bodies) represented up to 42.27% of the grid cells (see Fig. 3).
Ecological conditions in wintering areas can have both
long- and short-terms effects on migratory birds (Fig. 2;
Gordo 2007). Conditions during the entire wintering stay
should affect the population’s survival, which in turn
affects the size of the population returning to the breeding
grounds during the next breeding season (Winstanley et al.
1974; Den Held 1981; Cavé 1983; Szép 1995; Foppen et al.
1999; Boano et al. 2004). Recorded arrival dates to the
breeding grounds through observational chance (Tryjanowski and Sparks 2001; Tryjanowski et al. 2005) as well
as competitiveness for earlier arrival among males to
obtain the best breeding territories (Forstmeier 2002;
Møller 2004) may also be affected. We included vegetation
productivity (i.e. NDVI) during those months between the
arrival and departure of trans-Saharan migrants birds to the
wintering grounds (Table 1) to evaluate this hypothesis.
Ecological conditions during the days or weeks immediately preceding departures (i.e., short-term) from
wintering grounds can also affect the overall body condition of birds (see Fig. 2; Loske 1990; Salewski et al.
2002b; Ottoson et al. 2005). During this short-term interval, migrant birds should accumulate necessary fat reserves
to endure the geographical barriers to be overcome during
their northward migration, such as the Sahara Desert and
the Mediterranean Sea (Moreau 1972). It is assumed that
the onset and progression of migration is primarily controlled by endogenous rhythms (Berthold 1996), but some
plasticity in the migratory programme exists that enables
the individual to adapt the migration to its body condition
(Biebach 1985; Biebach et al. 1986). Body condition can
also affect the migration progress through flight performance (Lind et al. 1999; Kullberg et al. 2000) and stopover
duration (Biebach 1985; Yosef et al. 2006). We evaluated
all of these potential effects, in addition to vegetation
productivity, during the month or months immediately
prior to departure from the wintering grounds (see Table 1
for exact periods for each species).
Ecological and weather conditions in passage areas play
an important role in the progression and stopover duration
of migrant birds (Fig. 2). Ecological conditions in stopover
sites can affect phenology directly through body condition
of the migrant birds. Better ecological conditions may
improve the foraging of individuals in stopover sites due to
higher food abundance and, consequently, reduce the time
necessary to replenish their fuel stores (Schaub and Jenni
2001). Ecological conditions in passage areas were evaluated using the NDVI and winter (December–March) North
Atlantic Oscillation (NAO). The NDVI measurements
evaluate total food availability in a certain year (i.e.
abundant vs. scarce), while NAO is a better representative
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204
measure of timing of spring events (i.e., advanced vs.
delayed). In seasonal environments, such as the passage
areas evaluated here, the absolute amount of resources can
be as important as their availability, since an abundant year
can be underexploited by migrant birds due to a delayed
phenology of environment. The NAO is an atmospheric
circulation index that describes a major driving force of the
northern hemisphere climate system, which largely determines the interannual variations of the winter climate in the
Northern Atlantic region (Hurrell 1995). Its influence has
been demonstrated in many ecological processes (Ottersen
et al. 2001), including the timing of spring events (e.g.
Menzel et al. 2005). North Atlantic Oscillation data were
obtained as monthly values from http://www.cru.uea.
ac.uk/cru/data/nao.htm.
Favourable weather can enormously enhance the progression of migration (Richardson 1978, 1990; Kaňuščák
et al. 2004; Ahola et al. 2004; Both et al. 2005) and can
prevent forced or prolonged stopovers. In order to evaluate
the potential effects of weather on the progression of spring
migration, we used monthly precipitation and mean temperature values in migration areas (i.e. NW Morocco and
SW Iberia). For NW Morocco, we gathered monthly data
from the Global Historical Climatology Network (Peterson
and Vose 1997), while the Spanish Instituto Nacional de
Meteorologı́a provided data for SW Iberia. Temperature and
precipitation records for available observatories from each
region were averaged monthly to obtain a single Moroccan
and Iberian time-series for the period 1982–2000.
Statistical analyses
We associated phenological time-series to a set of ten
explanatory variables: NDVI during the overwintering stay
and at departure time in western Africa (two variables);
NDVI, temperatures and precipitation in NW Morocco
(three variables) and SW Iberia (three variables); winter
NAO (one variable); year (one variable). Year was included to take into account long-term temporal trends in
arrival dates (Gordo and Sanz 2006). Selected periods for
each variable were adjusted to the phenology of each
species (Table 1). In time-series for early populations of
the Cuckoo, Common Swift and Barn Swallow, variables
for SW Iberia were not included because this is the
breeding area of those populations.
An information–theoretic framework was employed to
determine the relative importance of each explanatory
variable on arrival dates, and multimodel inference was
used to estimate the parameters. All possible regression
models (1024 or 128, when ten or seven explanatory
variables were used, respectively) were constructed and
ranked according to the Akaike’s information criterion
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J Ornithol (2008) 149:199–210
(AIC) obtained with the GLZ module of STATISTICA
software (StatSoft 2001). The AIC is a function of a
measure of fit (maximum likelihood) and the number of
parameters in the model. If more explanatory variables are
used, the model fits better, but the penalty for extra variables is higher as well. The AIC provides a quantitative
measure of the relative support for each competing
hypothesis, i.e. each model, in a model selection framework. Since the number of years of arrival time series (18)
is less than 40-fold the number of explanatory variables
(ten or seven), a correction bias term was added to AIC
values to obtain a second-order criterion: AICc (Burnham
and Anderson 2002). Akaike weights for each model were
calculated from AICc according to the formulation purposed by Burnham and Anderson (2002). These weights
can be interpreted as approximate probabilities that a certain model is the best model in the set of considered
models. The relative importance of each explanatory variable was estimated by summing Akaike weights over all
the models in which the variable appears.
Parameters for each explanatory variable were estimated
on the basis of the entire set of models using a weighted
average. Model-averaged estimation has better precision
and reduced bias compared to the estimation from only the
best model (Burnham and Anderson 2002). This approximation is especially recommended when no single model is
overwhelmingly supported by data, i.e. when the AIC
values for best-ranked models are nearly equal (Johnson
and Omland 2004). Parameters for all possible regression
models were calculated with the GRM module of STATISTICA software (StatSoft 2001). To have an idea of the
proportion of variance accounted for by models, we also
calculated a weighted average of the adjusted R2.
Results
In four cases (White Stork, early Cuckoo and both early
and late Barn Swallow) the most important variable (i.e.
largest weight) was the year (Table 2). Therefore, there
were temporal trends in arrival dates during the study
period. In all of these cases, the effect of year was negative
[White Stork b = -1.614 ± 0.141 (SE); early Cuckoo
b = -0.613 ± 0.214; early Barn Swallow b = 0.614 ± 0.171; late Barn Swallow b = -0.240 ± 0.105],
which implies an advance of arrival dates between 1982
and 2000. Models in these four cases also showed the
highest explanatory capacity (Table 2). In the case of the
early Cuckoo and late Barn Swallow, the NDVI during the
wintering stay in Africa also showed a notable weight
(Table 2), which denotes the inclusion of this variable in
most of the best ranked models. In both cases, the effect of
the NDVI in Western Africa during all of the previous
J Ornithol (2008) 149:199–210
205
Table 2 Relative importance of explanatory variables
White Stork
Cuckoo
Common Swift
Barn Swallow
Nightingale
Early
Late
Early
Late
Early
Late
Year
1.000
0.860
0.166
0.236
0.221
0.987
0.655
0.204
NDVI during winter
0.527
0.575
0.186
0.239
0.269
0.315
0.619
0.226
NDVI at departure time
0.581
0.326
0.184
0.233
0.234
0.202
0.346
0.194
NDVI NW Morocco
0.407
0.296
0.174
0.233
0.246
0.165
0.184
0.417
Temperature NW Morocco
0.136
0.479
0.719
0.504
0.215
0.975
0.169
0.335
Precipitation NW Morocco
0.359
0.359
0.843
0.344
0.292
0.264
0.190
0.432
NDVI NW Iberia
Temperature NW Iberia
0.557
0.623
0.297
0.223
0.232
0.558
Precipitation NW Iberia
0.648
0.312
0.219
Winter NAO
0.164
0.185
0.162
0.280
0.379
0.261
0.168
0.178
Adjusted R2
0.928
0.527
0.480
-0.006
0.152
0.641
0.502
0.106
0.163
0.259
0.191
0.674
0.681
0.182
NDVI, Normalized difference vegetation index; NAO, North Atlantic Oscillation
The shown values are the sum of Akaike weights of explanatory variables over all models in which they appeared. Higher values mean that the
variable appeared in the best-ranked models. Early populations of the Cuckoo, Common Swift and Barn Swallow did not include explanatory
variables from SW Iberia
88
86
84
82
80
78
76
74
72
70
68
66
years with higher temperature in Moroccan pass areas (late
Cuckoo b = -1.797 ± 0.695; early Swift b = 0.304 ± 0.322; early Barn Swallow b = -3.006 ± 0.910).
Temperature in SW Iberia was the variable with the highest
weight for the late Swift and the Nightingale (Table 2), and
it also had a negative effect (late Swift b = 0.835 ± 0.409; Nightingale b = -0.496 ± 0.256). However, the explanatory capacity of regression models for the
Swift (both early and late) and Nightingale was poor
(especially for the early Swift; see Table 2), which diminishes the importance of the above-mentioned temperature
effects. Precipitation showed low weights, with the exception of for the late Cuckoo (Table 1).
Discussion
We are still far from knowing with precision how environmental conditions during the winter influence the
Mean arrival date to Spain
Fig. 4 Scatter plots between
mean first arrival dates to Spain
of the early Cuckoo and the
Barn Swallow against winter
NDVI values in western Africa.
High vegetation productivity
was related to early arrivals in
both cases
Mean arrival date to Spain
winter was negative (early Cuckoo b = -0.365 ± 0.205;
late Barn Swallow b = -0.401 ± 0.154). Hence, earlier
arrivals were recorded after winters with better ecological
conditions (Fig. 4). The weights of NDVI at departure time
in western Africa indicated, in most cases, that this variable
had little effect. Only in the case of the White Stork, this
variable shown to have a certain weight—a negative relationship to arrival dates (b = -1.015 ± 0.298). However,
for the White Stork, the importance of this variable as well
as NDVI during the entire wintering stay and NDVI and
weather in SW Iberia was absolutely overridden by the
strong effect of year.
The NDVI in the passage areas and the winter NAO index
never obtained high weights (Table 2), although weather did
achieved an elevated weight in some cases. Temperature in
NW Morocco was very important for the best models for the
early Barn Swallow, and it was also relevant for the late
Cuckoo and the early Swift models (Table 2). In all three
cases the effect was negative, i.e. earlier arrivals in those
Early cuckoo
158 160 162 164 166 168 170 172
Wintering NDVI in Western Africa
174
102
100
98
96
94
92
90
88
86
84
82
80
158
Late barn swallow
160
162
164
166
168
Wintering NDVI in Western Africa
170
123
206
subsequent stages of the life cycle in most of the transSaharan migratory bird species (Salewski and Jones 2006).
Only recently have some investigators compared the timeseries available from satellite measurements to those of
biological parameters (e.g. Pettorelli et al. 2005). Even
fewer studies have used NDVI values of the wintering
quarters (Møller 2004; Saino et al. 2004a, b; Szép and
Møller 2004; Møller and Szép 2005; Szép et al. 2006) to
assess the effect of ecological conditions during this period
of migratory birds’ life cycle to other life cycle events. Our
results show that better ecological conditions (i.e. higher
NDVI values) advance migratory phenology—although
only in some species. These effects were linked to conditions in the wintering areas, since vegetation productivity
measurements from passage areas were found to be of no
relevance in all of the cases studied here. Although our
results for the late Barn Swallow are fully in agreement
with those of Saino et al. (2004b), this case does not seem
representative for most of the long-distance migrants we
studied. Other species phenology was more affected by
temperature in the passage areas or even precipitation. One
of the most notable results of our study is the idiosyncrasies
found among species (or even populations) in the hypothetical environmental mechanisms that affect spring
phenology. This suggests that caution must be taken in
generalizing the results on the effects of NDVI on spring
arrivals from a single species and/or population (e.g. Saino
et al. 2004b).
Most of the earlier studies that tested the influence of the
environmental conditions in the wintering areas on transSaharan migratory birds used meteorological variables,
such as precipitation (Winstanley et al. 1974; Svensson
1985; Kanyamibwa et al. 1990, 1993; Peach et al. 1991;
Møller 1994; Barbraud et al. 1999; van den Brink et al.
2000; Gordo and Sanz 2006) or temperature (Kaňuščák
et al. 2004; Gordo et al. 2005; Chernetsov and Huettmann
2005; Rodrı́guez-Teijeiro et al. 2005) since these variables
are the only ones available before 1981. The results of
these studies (especially those in which precipitation in the
Sahel was used) agree fully with the negative effect of
NDVI in western Africa found in our study. In arid regions,
like the Sahel, water is the key factor in determining plant
productivity and thus resource availability (Eklundh and
Olsson 2003; Herrmann et al. 2005). The NDVI is well
suited for measuring intra- and interannual changes in
primary production as a result of seasonality and annual
trends in rainfall (Nicholson et al. 1990). From a long-term
point of view, the Sahel is only now recovering slowly
from a severe drought that persisted up to the 1980s
(Hulme et al. 2001; Nicholson 2001). The recent increase
in the amount of precipitation is closely related to the
increase in NDVI values recorded in that region (Eklundh
and Olsson 2003; Herrmann et al. 2005). In this sense,
123
J Ornithol (2008) 149:199–210
improved ecological conditions in winter quarters during
recent years may be partially responsible for the observed
advancement in spring arrivals of some species (see
Table 2; Gordo and Sanz 2006; Jonzén et al. 2006).
Therefore, not only would the increase in European temperatures be a potential factor influencing the advancement
of phenology in migrant birds, as many studies have
pointed out (Crick 2004; Lehikoinen et al. 2004), but
improved precipitation in Western Africa should also be
taken into account, at least for some long-distance migratory birds species and/or populations.
In our hypothetical framework (see Fig. 2), there are
two non-exclusive paths to explain how the NDVI in
African wintering quarters is affecting migrant birds to
induce changes in their migratory phenologies. On the one
hand, better ecological conditions may enhance individuals’ body condition prior to the beginning of pre-nuptial
migration. This will affect, in turn, those variables that
directly affect arrival date, i.e. departure time, speed of
migration and time spent on stopovers. On the other hand,
better ecological conditions during the entire winter would
increase survival and improve the moulting process. This
could result in larger populations, which increase the
chance of observing earlier arrivals of individuals to the
breeding grounds (Tryjanowski and Sparks 2001). In
addition, larger populations in wintering areas would
increase the selective pressures for earlier arrivals to the
breeding grounds because there would be a greater competition for high-quality territories between males
(Forstmeier 2002; Møller 2004). Unfortunately, our dataset
contains too few species to carry out a quantitative
assessment of the long- versus short-term effect hypotheses. Nevertheless, ecological conditions at the departure
time from Africa had a certain level of relevance in only
one (the White Stork) of the seven time-series analysed
here.
Interestingly, the effects of ecological conditions in the
passage areas on spring phenology were negligible (see
Table 2), both in terms of the amount of the vegetation
produced (i.e. NDVI) and the phenological development
state (i.e. NAO) of ecosystems in the migration areas. This
latter result disagrees with most of those reported earlier,
which found a strong effect of NAO on arrival dates
(Forchhammer et al. 2002; Hubálek 2003, 2004; Hüppop
and Hüppop 2003; Vähätalo et al. 2004; Stervander et al.
2005; Rainio et al. 2006). However, all of these studies
were carried out at high (or very high) latitudes of central
and northern Europe where birds had already migrated
much longer distances under NAO effects than those in SW
Europe. Hence, there is a much greater chance that the
NAO will affect the migrants that spend a long part of the
migratory journey passing through Europe, especially those
migrating by the western (i.e. Atlantic) route. Only one
J Ornithol (2008) 149:199–210
recent study (Jonzén et al. 2006) has reported a positive
effect of NAO on the arrival time of migrants at the Italian
island of Capri. Given these contradictory results, further
research is urgently needed for areas different from
northern European latitudes (e.g. Jonzén et al. 2006;
MacMynowski and Root 2007) because the geographical
variability of arrival dates in response to NAO remains to
be explored (Zalakevicius et al. 2006). The apparent noneffect of the ecological conditions in the passage areas
reported in our study suggests, at least for Spanish populations of migrant birds, a direct journey from wintering
grounds without stop-overs and refuelling in northern
Africa (Moreau 1961; Pilastro and Spina 1997). However,
temperature in the passage areas (both NW Africa and SW
Iberia) did have noteworthy weights in many cases, suggesting that en route effects on the first arrival dates would
be more mediated by weather conditions in the passage
areas (Ahola et al. 2004; Both et al. 2005) than by ecological conditions there. Better weather conditions would
allow a faster progression.
Our study confirms that present performance is affected
by the past life history of individuals (Marra et al. 1998;
Sillett and Holmes 2002; Hogstad et al. 2003; Norris et al.
2004). In our case, interannual fluctuations in spring
arrivals of some long-distance migratory birds were influenced by plant productivity several months before in an
area thousands of kilometres from where individuals breed.
Therefore, we should consider the entire life cycle of a bird
species as a continuum that requires more integrative
studies linking all parts of the cycle. More efforts are
needed to investigate the wintering ecology of trans-Saharan migratory birds in order to understand temporal
population changes recorded in Europe.
Zusammenfassung
Die relative Bedeutung der Bedingungen
in Überwinterungs- und Durchzugsgebieten
für die Ankunftsdaten im Frühjahr:
der Fall iberischer Langstreckenzieher
Vorherige Studien haben anhand von Fernmessungsdaten
die Effekte der winterlichen ökologischen Bedingungen in
Afrika auf biologische Parameter, die während der nachfolgenden Brutsaison in Europa aufgenommen wurden,
abgeschätzt. Basierend auf diesen Ergebnissen erwarteten
wir, dass eine hohe Primärproduktion im Winter und die
resultierende hohe Ressourcenverfügbarkeit die Ankunft
von Langstreckenziehern in ihren europäischen Brutgebieten
aufgrund
der
verbesserten
ökologischen
Bedingungen verfrühen sollte. Wir haben die Schwankungen in den frühesten Ankunftsdaten von fünf Arten
207
(Weißstorch, Kuckuck, Mauersegler, Rauchschwalbe und
Nachtigall) auf der Iberischen Halbinsel zwischen 1982
und 2000 in Beziehung zu mehreren Variablen untersucht:
ökologische Bedingungen in den afrikanischen Überwinterungs- und Durchzugsgebieten, wiedergegeben durch den
normalisierten differenzierten Vegetationsindex (NDVI),
Temperatur und Niederschlag in den Durchzugsgebieten
sowie winterliche Nordatlantische Oszillation (NAO). Die
ökologischen Bedingungen in den Überwinterungsgebieten
waren für die Phänologie von Weißstorch, Kuckuck und
Rauchschwalbe von Bedeutung, während der NDVI in
Durchzugsgebieten sowie die NAO bei keiner der Arten
einen Effekt zeigte. Zugvögel kehrten nach Wintern mit
hoher Primärproduktion in Afrika früher in ihre Brutgebiete zurück. Die Temperatur in den Durchzugsgebieten
spielte für später ziehende Arten (d.h. Kuckuck, Mauersegler und Nachtigall) eine Rolle, obwohl in allen Fällen
die Wichtigkeit dieses Faktors wegen der niedrigen
Aussagekraft der entsprechenden Modelle geringer einzuschätzen ist. Diese Arten wurden im Frühjahr solcher
Jahre mit hohen Temperaturen in den Durchzugsgebieten
früher im Brutgebiet nachgewiesen. Wir nehmen an,
dass der Zusammenhang zwischen afrikanischem
NDVI und Ankunftsphänologie durch einen Anstieg der
Überlebensraten im Winter und/oder ein schnelleres
Erreichen der prämigratorischen Körperkondition und eine
schnellere Passage durch die Sub-Sahara-Gebiete zustande
kommt.
Ackowledgements This work would not be possible without the
hundreds of volunteers of the Spanish phenological network. We
thank the INM for access to the phenological data, Jorge M. Lobo for
providing NDVI data and the Oficina de Anillamiento for the information on African ringing recoveries of Spanish birds. The useful
suggestions of Bruno A. Walther and two anonymous reviewers
improved a first draft of the manuscript. O.G. was supported by a
fellowship of the FPU programme of the Spanish MEC (ref. AP20021439). J.J.S. was supported by the Spanish MEC (project REN-20010611/GLO).
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APPENDIX (Supplementary material)
2
Pearson correlations among explanatory variables included in models of white stork (see Table 1) for
the period 1982-2000 (n = 18). In bold significant values (P < 0.05).
4
NDVI during winter
NDVI at departure time
NDVI NW Morocco
Temperature NW Morocco
Precipitation NW Morocco
NDVI NW Iberia
Temperature NW Iberia
Precipitation NW Iberia
Winter NAO
Year
0.654
0.585
0.310
0.246
0.098
0.335
0.013
-0.054
0.017
NDVI
during
winter
NDVI at
departure
time
NDVI NW
Morocco
Temperature
NW Morocco
0.884
0.404
0.212
0.215
0.391
0.397
-0.029
0.038
0.152
0.199
0.110
0.113
0.425
-0.039
-0.021
0.415
0.176
0.854
0.118
0.271
-0.049
0.247
0.269
0.235
0.487
-0.407
6
1
Precipitation NDVI NW
NW Morocco
Iberia
0.044
0.464
0.830
-0.705
-0.144
0.162
0.042
Temperature
NW Iberia
Precipitation
NW Iberia
0.258
-0.303
-0.819
Pearson correlations among explanatory variables included in models of early cuckoo and early barn
2
swallow (see Table 1) for the period 1982-2000 (n = 18). In bold significant values (P = 0.05).
NDVI during winter
NDVI at departure time
NDVI NW Morocco
Temperature NW Morocco
Precipitation NW Morocco
Winter NAO
Year
0.654
0.541
0.199
0.266
-0.088
0.017
NDVI
during
winter
NDVI at
departure
time
NDVI NW
Morocco
Temperature
NW Morocco
Precipitation
NW Morocco
0.885
0.347
0.250
0.055
0.027
0.207
0.141
0.143
-0.160
0.151
0.226
-0.288
-0.487
0.253
-0.330
4
2
Pearson correlations among explanatory variables included in models of late cuckoo and late barn
2
swallow (see Table 1) for the period 1982-2000 (n = 18). In bold significant values (P = 0.05).
NDVI during winter
NDVI at departure time
NDVI NW Morocco
Temperature NW Morocco
Precipitation NW Morocco
NDVI NW Iberia
Temperature NW Iberia
Precipitation NW Iberia
Winter NAO
Year
0.660
0.395
0.021
0.262
-0.005
0.117
0.488
-0.031
0.017
NDVI
during
winter
NDVI at
departure
time
NDVI NW
Morocco
Temperature
NW Morocco
0.795
0.253
0.157
0.301
0.321
0.406
0.025
-0.009
0.405
-0.137
0.517
0.293
-0.035
-0.147
-0.273
-0.243
0.090
0.828
-0.156
-0.391
-0.337
-0.494
-0.194
0.479
-0.055
0.062
4
3
Precipitation NDVI NW
NW Morocco
Iberia
-0.041
0.002
-0.123
-0.174
0.014
-0.086
-0.148
Temperature
NW Iberia
Precipitation
NW Iberia
-0.198
0.380
0.235
Pearson correlations among explanatory variables included in models of early common swift (see
2
Table 1) for the period 1982-2000 (n = 18). In bold significant values (P < 0.05).
NDVI during winter
NDVI at departure time
NDVI NW Morocco
Temperature NW Morocco
Precipitation NW Morocco
Winter NAO
Year
0.654
0.541
0.199
0.266
-0.088
0.017
NDVI
during
winter
NDVI at
departure
time
NDVI NW
Morocco
Temperature
NW Morocco
Precipitation
NW Morocco
0.885
0.347
0.250
0.055
0.027
0.207
0.141
0.143
-0.160
0.151
0.226
-0.288
-0.487
0.253
-0.330
4
4
Pearson correlations among explanatory variables included in models of late common swift (see
2
Table 1) for the period 1982-2000 (n = 18). In bold significant values (P < 0.05).
NDVI during winter
NDVI at departure time
NDVI NW Morocco
Temperature NW Morocco
Precipitation NW Morocco
NDVI NW Iberia
Temperature NW Iberia
Precipitation NW Iberia
Winter NAO
Year
0.620
0.301
-0.057
0.431
-0.183
-0.050
0.348
0.301
0.017
NDVI
during
winter
NDVI at
departure
time
NDVI NW
Morocco
Temperature
NW Morocco
0.783
0.262
0.206
0.173
0.289
0.179
-0.064
-0.048
0.505
0.127
0.236
0.263
0.137
-0.387
-0.279
-0.170
-0.260
0.628
-0.135
-0.456
-0.285
-0.149
-0.211
0.557
-0.137
-0.148
4
5
Precipitation NDVI NW
NW Morocco
Iberia
-0.216
0.261
-0.098
-0.060
-0.392
-0.066
-0.183
Temperature
NW Iberia
Precipitation
NW Iberia
-0.097
-0.013
0.214
Pearson correlations among explanatory variables included in models of nightingale (see Table 1) for
2
the period 1982-2000 (n = 18). In bold significant values at (P < 0.05).
NDVI during winter
NDVI at departure time
NDVI NW Morocco
Temperature NW Morocco
Precipitation NW Morocco
NDVI NW Iberia
Temperature NW Iberia
Precipitation NW Iberia
Winter NAO
Year
0.660
0.217
0.003
0.124
-0.064
0.045
0.061
-0.020
0.017
NDVI
during
winter
NDVI at
departure
time
NDVI NW
Morocco
Temperature
NW Morocco
0.659
0.223
-0.080
0.146
0.261
-0.063
-0.145
-0.009
0.511
-0.053
0.034
0.410
0.096
-0.523
-0.245
-0.169
-0.482
0.745
-0.161
-0.547
-0.273
-0.351
-0.116
0.718
-0.147
-0.286
4
6
6
Precipitation NDVI NW
NW Morocco
Iberia
-0.398
0.137
0.070
0.228
-0.134
-0.319
-0.172
Temperature
NW Iberia
Precipitation
NW Iberia
-0.404
-0.355
0.441