Behavioral Ecology
doi:10.1093/beheco/ari046
Advance Access publication 13 April 2005
The role of wind in passerine autumn
migration between Europe and Africa
Birgit Erni, Felix Liechti, and Bruno Bruderer
Swiss Ornithological Institute, CH-6204 Sempach, Switzerland
Large ecological barriers such as oceans and deserts have considerably shaped the migratory strategies of birds. The ecological
barriers posed by the Alps, the Mediterranean Sea, and the Sahara seem to prevent most long-distance migrants from flying on
a direct southward course from Europe to Africa. Migratory routes toward southwest and southeast prevail. These two flyways
differ with respect to topography, refueling possibilities, and wind conditions. Aiming at a better understanding of the evolution
of both flyways in spite of differing conditions, we studied potential survival of passerine birds on their first autumn migration
from northern Europe to tropical Africa by means of a computer simulation. Considering real wind conditions at 850 mb
(approximately 1500 m above sea level), the survival rates of birds with southeasterly (SE) migratory directions were much higher
than those of birds with southwesterly (SW) directions. With the possibility to choose the altitude (from four levels) with the most
favorable wind, both SE and SW migrants had similar high survival, but only with refueling opportunities in northwest (NW)
Africa for SW migrants. Our results suggest that the southwestern flyway depends on the selection of days, but especially altitudes,
with favorable wind conditions and on refueling opportunities in NW Africa. The SE flyway is privileged by the frequent favorable
wind conditions for crossing the eastern Mediterranean Sea and the Egyptian desert, where refueling sites are almost absent.
Both autumn migration routes would be unlikely without wind assistance. Key words: autumn migration, ecological barriers,
migration routes, passerine migration, Sahara, simulation, wind. [Behav Ecol 16:732–740 (2005)]
ost long-distance bird migrants have to cross or
circumvent large ecological barriers such as oceans and
deserts, for example, Gulf of Mexico and Sahara. The
European-African bird migration system is particularly suitable for the study of behavioral adaptations to difficult
ecological conditions for several reasons: three ecological
barriers of different severities (the Alps, the Mediterranean
Sea, and the Sahara Desert) are arranged one after the other
across the shortest possible migration route between Central
Europe and the African Savannahs. Ring recovery data suggest
that direct southward migration is rare (Zink, 1973–1985;
Zink and Bairlein, 1995). Most long-distance migrants from
Western Europe migrate in southwesterly (SW) directions
toward West Africa. The number of species and populations
taking southeasterly (SE) routes increases with longitude.
Even within species, westerly populations usually migrate SW,
while their easterly conspecifics take the SE route, resulting in
a so-called migration divide for such species in Europe, even if
their winter quarters in Africa converge. One hypothesis is
that the (recent) ecological conditions induced by the
barriers would be sufficient to cause the evolution of the
two different flyways, even if the original bird populations
started with southerly directions (e.g., Alerstam, 2001). A
second hypothesis is that the existence of the two main
flyways is not the result of recent evolution: vegetation
history since the last glaciation some 20,000 years ago (e.g.,
Frenzel et al., 1992) suggests that the avian colonization of
Europe started mainly from refugia in the western and eastern
Mediterranean, circumventing the Alps, where the recovery
from glaciation took longer than in the plains. Birds might
M
Address correspondence to B. Erni, who is now at the Department
of Mathematics and Statistics, P.O. Box 3045 STN CSC, University
of Victoria, Victoria, British Columbia V8W 3P4, Canada. E-mail:
[email protected].
Received 10 December 2003; revised 1 March 2005; accepted 15
March 2005.
The Author 2005. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved.
For permissions, please e-mail: [email protected]
follow such ancient routes of range expansion to move
between breeding and nonbreeding ranges (Bruderer, 1997;
Sutherland, 1998). Many migration divides in Europe would
be in line with such an explanation, and some migratory
routes are hard to explain otherwise (examples in Bruderer,
1997; Sutherland, 1998).
Several studies have pointed out the likely importance of
favorable wind conditions for successful migration, especially
for crossing ecological barriers: for example, migration of
waders from West Africa to western Europe in spring (Piersma
and van de Sant, 1992) and from Australia to Asia (Tulp et al.,
1995) and migration of waders and passerines from North
America to South America (Butler et al., 1997; Nisbet et al.,
1995; Richardson, 1980; Stoddard et al., 1983; Williams et al.,
1974). Very few studies have investigated the feasibility of
a chosen migration route compared to alternative routes.
While satellite tracking has shed significant light on the
migration of large birds, such as storks, cranes, ducks, geese,
and raptors (Berthold, 2001, and references therein),
knowledge of the long-distance migration of small passerines
remains scarce, being mainly restricted to studies performed
at stopover sites close to or within ecological barriers (e.g.,
Bairlein, 1992; Biebach, 1995; Schaub and Jenni, 2000). As it
is difficult to follow evolutionary processes in the field,
particularly if they involve phenomena taking place on
a continent-wide scale, we chose computer simulations to
explore the evolutionary potential of migratory behavior in
relation to environmental factors. We simulated the first
autumn migration of small passerines migrating from
northern/central Europe to south of the Sahara. In a previous
study, we analyzed the influence of stopover behavior and
flight rules on the prospective survival rates of simulated
migrating birds (Erni et al., 2003). For the present study, we
extended this previous model by integrating wind patterns
migrants encounter en route. This allowed us to explore the
role of wind patterns in the evolution and retention of
possible flyways as well as the possible benefit of behavioral
adaptations to wind conditions.
Erni et al.
•
Wind in passerine autumn migration
Figure 1
Map of Europe and North Africa with topographical features used in
simulations. It is for illustrative purposes only, with 1 longitude and
1 latitude of equal length throughout. Shaded areas represent the
Alps and the Sahara Desert, and black dots represent islands, where
refueling was assumed possible throughout the grid cell. Arrows
represent monthly long-term mean wind vectors for September at
a pressure level of 850 mb (approximately 1500 m a.s.l.), with the
direction of the arrow representing wind direction; the length of the
vector is more an indication of the variability of wind conditions than
mean wind speed, short vectors indicating variable conditions; data
from NOAA–CIRES Climate Diagnostics Center: http:// www.cdc.
noaa.gov/cdc/reanalysis/. Only every second wind vector of the
original data is depicted here. The inset gives a scale for the vector
lengths. Two tracks of successful birds are indicated, one with
migratory flight direction 207 and the other with 160 . Both these
had an orientation error of 15 .
METHODS
We extended the simulation model of Erni et al. (2003),
adding wind patterns and enlarging the geographic area
considered slightly toward the east (Figure 1). Model birds
were assigned parameters representing strategies (e.g., endogenous direction ¼ genetically determined flight direction)
and state variables (e.g., fuel level and geographic position).
The model birds ‘‘migrated’’ over the simulated environment,
a two-dimensional representation of real topography, refueling
conditions, and wind (Figure 1). We focused on the migration
of first-year nocturnal passerine migrants. Many of our
assumptions are based on estimates for the garden warbler,
Sylvia borin (e.g., flight range, fuel deposition rates). This
species is a common passerine long-distance migrant of average
size, used previously as a basis for model predictions (e.g.,
Klaassen and Biebach, 1994). Smaller birds would have a shorter
flight range with a given fuel load but higher refueling rates
(Lindström, 1991). Changing these parameters could influence the survival rates calculated in our simulations but is
unlikely to change our main results and conclusions.
Topography, refueling conditions, and geomagnetism
The environment was modeled as a two-dimensional grid map
with resolution 1 latitude by 1 longitude, defined between
65 N to 0 N and 22 W to 53 E (Figure 1). This grid map
consisted of four layers: topography, refueling rates, magnetic
declinations, and wind. A topographical map formed the first
layer; each grid cell was assigned one of the following topographical features: land, desert, semidesert, water, coastline,
733
Alps, island, or northwestern Africa. Coastlines were defined
to lie in a NW–SE, NE–SW, N–S, or W–E direction. To each of
these grid cells we assigned a fuel deposition rate, determining the rate of mass gain relative to lean body mass
(g/day/lbm ¼ grams per day as a proportion of lean body
mass) for a bird stopping over in that grid cell. Fuel
deposition rates were randomly assigned at the beginning of
each simulation to land and coastline grid cells (except where
the coastline coincided with the desert, in which case it was
assigned a zero fuel deposition rate; shaded area in Figure 1)
from a uniform distribution with limits 0.01 and 0.09 g/day/
lbm, a range observed in migrating passerines (Lindström,
1991). The lower limit was set at 0.01 g/day/lbm rather than
zero because we assumed that in a land grid cell of approximately 100 by 100 km a passerine migrant will always find
some refueling opportunities. Exceptions are specialized
feeders that rely on patchily distributed resources. Fuel
deposition rates were set to zero over water, desert, and
Alps, to 0.02 g/day/lbm over semidesert, and to 0.03 g/day/
lbm over islands and in the unshaded region of NW Africa
(Figure 1). The zero value for the Alps was chosen to
emphasize their character as a barrier, which, however, stems
from their height rather than from a lack of food. The rate of
0.03 g/day/lbm for NW Africa was chosen because we
assumed refueling rates to be lower than the average on
mainland Europe. The same fuel deposition rate was assigned
to islands to account for their smaller area and possibly
increased competition due to a concentration of migrants.
Fuel deposition rates measured by Schaub and Jenni (2000)
on recaptured birds were generally lower than the values we
have assumed here. Decreasing the average fuel deposition
rate would increase the total time taken for migration (Erni
et al., 2002b). More detailed estimates of refueling rates are
currently not possible as field experiments show that there is
large variation between individuals, sites, and years (Schaub
and Jenni, 2000).
Third, the magnetic declination (the angle between
magnetic north and true north) for each grid cell of 1
latitude by 1 longitude was estimated with the World
Magnetic Model 2000 (U.S. Geological Survey: http://
geomag.usgs.gov/geomag/geomagAWT.html). Magnetic declinations were needed because we modeled birds migrating
in a constant compass direction (Mouritsen, 1998). Including
magnetic declinations had the effect of a slight easterly shift in
flight directions over western Africa; the maximum magnetic
declination over a land grid cell in the area of Figure 1 was 10
at 12 N, 17 W, decreasing along a gradient toward the NE.
Wind data
The fourth layer was a wind vector for each grid cell. Winds
have a measurable and predictable effect on flight (Liechti,
1995). As winds vary considerably over the area considered in
speed and direction, we used actual data instead of
probabilistic models of wind conditions. These wind vectors
are interpolated values based on actual wind measurements
taken at many locations across the globe. The data are
available on the World Wide Web (NOAA–CIRES Climate
Diagnostics Center: http://www.cdc.noaa.gov/cdc/reanalysis/,
National Center for Atmospheric Research: http://dss.
ucar.edu/). We used three types of wind data: (1) Longterm monthly mean wind vectors for July to November at
a pressure level of 850 mb corresponding to approximately
1500 m above sea level (a.s.l.), one vector for each 2.5
latitude by 2.5 longitude grid cell and for each month, with
the mean vector for each cell and month calculated from
approximately 50 years of data. Mean wind vectors in
September are illustrated in Figure 1. Winds at 850 mb are
Behavioral Ecology
734
Figure 2
Frequency diagrams of wind
directions at 20 locations
shown at the four pressure
levels 1000, 925, 850, and 700
mb. Bars point to directions
into which winds blow. Each
individual frequency rose diagram was calculated from midnight (UTC) wind directions
of September and October
over years 1999–2003. The underlying map is as in Figure 1.
over large areas free of orographic distortions and are often
used to characterize wind conditions for migrating birds. The
925-mb pressure level surface at about 750 m a.s.l. would be
closer to frequent bird migration altitudes but is more
exposed to orographic distortions and often not available.
(2) Daily mean wind vectors for the autumn seasons of 1990–
2000 with a spatial resolution of 5 latitude by 5 longitude,
also at a pressure level of 850 mb. (3) Daily wind vectors for
the autumn seasons of 1999–2003 measured at midnight (0 h
coordinated universal time [UTC]), with a spatial resolution
2.5 latitude by 2.5 longitude, at four pressure levels: 1000,
925, 850, and 700 mb roughly representing wind conditions at
100, 750, 1500, and 3000 m a.s.l., respectively (Figure 2). The
package netCDF for the software R was used to read and
organize the wind data.
Flight range
The range, Y, a bird could fly with a given fuel load, x (fuel
mass relative to lean body mass), was defined as
1
Y ðxÞ ¼ c 1 pffiffiffiffiffiffiffiffiffiffiffi
ð1Þ
1þx
(Alerstam and Lindström, 1990), where c is the flight range
constant, which depends, among other factors, on the energy
density of the fuel (i.e., relative amounts of fat and protein)
and the bird’s aerodynamic properties (Alerstam and
Hedenström, 1998). We assumed a value of c ¼ 10,000 km,
which, to our knowledge, is a generous estimate for a small
passerine bird; Lindström and Alerstam (1992) estimated a
value of c ¼ 8500 km for bluethroats, Luscinia svecica. The fuel
load, x, was limited to values 0.9 (i.e., fuel loads 90% of
lean body mass), assuming that passerines can almost double
their weight when preparing for a flight phase, and to values
0. With c ¼ 10,000 km, the maximum flight range, that is,
with a fuel load of 0.9 relative to lean body mass, is
approximately 2750 km.
Stopover, refueling, and wind selection
To simulate flight and feeding phases during migration, we
modeled two stopover strategies. When no winds were
included in the simulations, stopover duration in areas with
refueling possibilities was set to 10 days during which the bird
gained mass according to the fuel deposition rate defined for
the given grid cell. After these 10 days the bird would fly for
two consecutive nights, resting during the day in between,
with its fuel load staying constant. After the two nights of
flight it would again stop for refueling and so on. We assumed
no time cost at the beginning of a new stopover (search/
settling time). With this stopover strategy, the fuel load
gradually increased over long distances with refueling
opportunities (Erni et al., 2003).
Migrating birds choose favorable wind conditions among
days (Erni et al., 2002a; Richardson, 1978, 1990). We modeled
this in the second stopover strategy as follows: the bird left the
stopover site in favorable wind conditions, defined by a
threshold wind profit level (see definition below) depending
on the bird’s fuel load. With fuel loads below 0.02 (2% of lean
body mass), the bird would never depart; with 0.02, it only
departed with wind profit levels 10 m/s. The threshold wind
profit level then decreased linearly to 5 m/s at a fuel load of
Erni et al.
•
Wind in passerine autumn migration
0.9. With fuel loads of 0.9 or a wind profit larger or equal to
the threshold level, the bird departed from the stopover site at
the end of the day. With this stopover strategy we additionally
assigned a maximum stopover duration of 14 days at any one
site. This time limit prevented birds from staying too long at
poor stopover sites and waiting too long for favorable wind
conditions (Weber and Hedenström, 2000). Also with this
strategy birds flew for two nights before another refueling
stopover. To select the altitude with the best wind conditions,
the altitude out of four with the largest wind profit value
(calculated with respect to the bird’s endogenous direction)
was chosen once per hour. The above assumptions on
selection of days and altitudes with favorable wind conditions
are based on empirical data, showing that birds avoid strong
headwinds for departure (Erni et al., 2002a) and select
heights with favorable tailwinds (Bruderer et al., 1995; Liechti
et al., 2000).
Empirical data show that birds obtain large fuel loads
before crossing ecological barriers (Bairlein, 1991; Fransson
et al., 2001). Therefore, we also examined the effect of
a threshold fuel level before the crossing of ecological barriers
(only in combination with the second stopover strategy, i.e.,
selecting days with favorable wind conditions). The threshold
fuel levels whose effects were examined were 0.9 (90% of lean
body mass) before crossing the Sahara Desert and 0.5 before
crossing the Mediterranean Sea in the case when refueling
was possible in NW Africa (unshaded area in Figure 1) but 0.9
before crossing the Mediterranean Sea in the case when fuel
deposition rates in NW Africa were zero.
Airspeed and flight time
Airspeed was set to 10.5 m/s, which is in the range of values
observed for small passerine birds (Bloch and Bruderer, 1982;
Bruderer and Boldt, 2001). We set flight time per night to 8 h
while over land and 12 h per night over desert and assumed
continuous flight while over water. While birds are still
migrating over Europe, flight time per night is mostly less
than 8 h per night (Zehnder et al., 2001). A decrease in flight
time per night would increase the total time taken for migration
but would have little effect on survival. We assumed that birds
over desert area flew only at night, they stopped during the day,
and their fuel load decreased by 0.01 per day (1% relative to
lean body mass) during the diurnal rest. This assumed value is
lower than measurements by Safriel and Lavee (1988). We
refrained from integrating nonstop flight over desert areas,
thus avoiding the need to model different flight conditions
(greater water loss and more turbulent air) during the day, even
though some migrants continue migration into the day or
(rarely) even throughout the day while crossing a desert
(Biebach et al., 2000; Bruderer B and Liechti F, personal
observations).
Flight directions
The existence of an endogenous (genetically determined)
direction has been confirmed in many bird species (Wiltschko R
and Wiltschko W, 2003, and references therein). It can be
assumed that most migrating passerine species have a genetically inherited migratory direction (Berthold, 1996) and rely
on this direction for a given time period, especially during
their first autumn migration (Mouritsen, 1998; Perdeck,
1958). In our simulations each individual was assigned an
endogenous migratory direction. The heading (the bird’s
flight direction in still air) was chosen at the beginning of
each flight step and every 4 h thereafter from a wrapped
normal distribution with mean equal to the bird’s endogenous direction and an angular deviation of 30 or 15 ,
735
corresponding to a mean vector length, r, of 0.863 and 0.966,
respectively (Batschelet, 1981). Little is known about individual variation in flight directions and nothing about the
frequency of adjustments. Estimates for variation in juveniles
range from 38 (angular deviation) in orientation cage experiments (Moore, 1984) to 21 in satellite-tracked juvenile
raptors (Thorup et al., 2003). We assumed constant magnetic
compass directions with respect to magnetic North. The bird’s
position was recalculated after every hour of flight. Tracks
(flight vector plus wind vector) were calculated assuming
a spherical earth with a radius of 6367 km (formulae from
Ed Williams: http://williams.best.vwh.net/avform.htm).
Starting values
In all simulations birds started from point 60 N, 15 E with
a fuel load (proportion of lean body mass) assigned from
a normal distribution with mean 6 SD equal to 0.3 6 0.05.
The departure day for each bird was chosen from a normal
distribution with mean day 25 (25 July) and a standard deviation of 3 days. The time limit for completing migration was
day 140 (17 November). Birds taking longer than this were
deemed unsuccessful. Too little is known about the costs of
arriving early or late in the wintering grounds to restrict this
time limit further.
Wind profit
To define the favorability of wind for a certain flight direction,
we chose a measure that accounts for both cross- and
headwinds (Erni et al., 2002a). Assuming that the aim of
a flying bird is to fly d m/s into its endogenous direction, with
d equaling airspeed, we defined wind profit as
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
wind profit ¼ d d 2 þ w 2 2dw cosðaÞ:
ð2Þ
The term under the root sign comes from using the cosine
rule, where a is the angle ‘‘endogenous direction wind
direction’’ (direction into which wind blows) and w is the
wind speed (m/s). Wind profit (m/s) measures the distance
per second the wind carries the bird toward its intended goal.
Negative values indicate that the wind would carry a stationary
bird further away from the goal, and positive values indicate
that the wind would carry a stationary bird toward the goal.
This formula does not work if the tailwind component is
larger than the airspeed of the bird. In such instances, we took
wind profit to equal the tailwind component minus the
absolute value of the crosswind component.
Simulated evolution of endogenous directions
To investigate which migratory directions would be feasible
first without winds and second with long-term mean monthly
winds, we allowed endogenous directions to ‘‘evolve’’ over
10 generations, with 10,000 birds in each generation (as in
Erni et al., 2003). We found that the patterns stabilized after
5–10 generations, after which mainly the extreme values
became less frequent, so decreasing the range of successful
directions. To the first generation we assigned a large range of
endogenous directions from a wrapped normal distribution
with mean 180 and angular deviation 40 . Migration was
simulated, and successful directions were considered those of
birds that had survived. In the second generation of 10,000
new model birds, each bird was assigned a value randomly
chosen from those directions that were successful in the
previous generation. No mutation or crossing of parent values
(resulting in intermediate values) was modeled because we
were more interested in the feasible range of directions than
736
Behavioral Ecology
in the actual evolutionary process. After 10 generations of this
process, the distribution of values that survived was assumed
to represent the distribution of feasible values under the given
environmental and behavioral settings.
Survival/Mortality
A simulated bird successfully completed migration when it
reached the last row of cells of the shaded area representing
the Sahara Desert (Figure 1) or when it had reached land cells
with refueling rates .0 g/day/lbm south of 20 N (Figure 1).
Birds that ran out of fuel over water, desert, or Alps or had not
completed migration after 140 days were unsuccessful
(mortality). The survival rate was calculated as the percentage
of successful birds.
RESULTS
Migration without and with long-term mean winds
We let endogenous directions evolve under two conditions,
one without winds and one under average wind conditions
(long-term monthly mean winds at 850 mb). After 10
generations in long-term monthly mean wind conditions,
the set of endogenous directions was SE with the following
distribution: 186.0 , 172.6 , 169.4 , 166.3 , and 155.2 (n ¼
8891) representing the most westerly, 25% quartile, median,
75% quartile, and most easterly direction, respectively. Of
the first generation of 10,000 birds 30% survived, of which
69% had endogenous directions east of 180 . In comparison,
endogenous directions that evolved without wind conditions
were SW (median, range, and quartiles in the order above:
212.5 , 207.3 , 205.9 , 204.3 , 197.2 , n ¼ 5550). In the first
generation 10% of the 10,000 simulated birds survived, of
which 88% had endogenous directions west of 180 . We
used the above two sets of directional values (SE and SW) as
migratory directions in further simulations, assuming that
they represented feasible directions for southeast and
southwest migrants, respectively. Further interpretation is
not meaningful, except that topography and the associated
distribution of refueling opportunities favored SW directions,
whereas in combination with mean wind conditions, SE
directions were favored. Long-term mean wind conditions at
one altitude (850 mb) are not representative of real wind
conditions because of the lack of variation and because mean
directional vectors become less representative with increasing
variation in the original data.
In the next step, we compared yearly survival rates between
these two ‘‘populations’’ of endogenous directions using daily
wind data at 850 mb measured during the autumns of 1990–
2000 (Figure 3). The average survival rate over these 11 years
for simulated birds with SE directions was 70.0 6 7.7% (mean 6
SD), higher than the survival rate of birds with SW directions
(30.3 6 8.4%), with a 95% confidence interval (CI) for the
yearly difference in mean survival rates of 30.8–48.5% (paired t,
n ¼ 11). The variation between repeated simulation runs
under the same conditions was small (5.8) compared to the
variation between years (64.2) (variance components; Sokal and
Rohlf, 1995).
Wind and migratory strategies
We next explored the influence of different stopover
strategies and orientation parameters on survival. Survival
rates, mortality causes, and total time taken for migration are
presented in Table 1. With a stopover duration of 10 days and
an orientation error of 30 , survival rates of simulated birds
with SW directions were lower (two-sample t 95% CI for
Figure 3
Survival rates (mean 6 SD) of simulated birds in 11 autumns with
wind data at 850 mb. Mean for each year calculated from 10
simulations with 500 birds each. SW and SE refer to two theoretical
populations, one with SW endogenous directions and the other with
SE endogenous directions. Stopover strategy: constant stopover
duration of 10 days, 2 nights flight, and no selection for favorable
winds (cf. Table 1, points 2 and 11).
difference in survival rates: 20–30%, n1 ¼ 11, n2 ¼ 10) with
winds at 850 mb than when flying without any winds (Table 1,
points 1 and 2). The additional mortality was due to running
out of fuel over the Sahara (Table 1). With winds, the median
of the arrival distribution shifted to the east by approximately
11 from 12.1 W to 0.9 W. Selection of favorable wind
conditions for departure (point 3), a rule of crossing
ecological barriers only with threshold fuel levels (0.5 for
the Mediterranean, 0.9 for the Sahara Desert; point 4), and
a decrease in orientation error to 15 (point 5) increased
survival rates, with the largest increase occurring with
a decrease in orientation error (95% CI for difference in
mean survival rate from previous strategy: 5–15%, 1–5%, and
9–16% for wind selection, threshold fuel, and orientation
error, respectively; n ¼ 11 in all three comparisons, paired t).
On the other hand, for simulated birds with SE directions,
winds at 850 mb increased survival rates by 59–69% (95% CI;
two-sample t; n1 ¼ 11, n2 ¼ 10; df ¼ 10.1) (Table 1, points 10
and 11). Selection for favorable winds when leaving stopover
sites (point 12) and a decrease in orientation error to 15
(point 14) further increased survival rates (95% CIs for mean
difference: 4–14% and 7–12%, respectively; paired t, n ¼ 11 in
both comparisons). However, fueling up to a threshold fuel
level (point 13) did not increase survival (95% CI: 2% to 6%,
n ¼ 11, paired t). With simulation parameters set as those in
points 5 and 14 in Table 1 (selection for winds, threshold fuel
levels, and orientation error of 15 ), survival rates of birds
with SE directions were 25–45% (95% CI, two-sample t, n1 ¼
n2 ¼ 11, df ¼ 12.3) higher than those for birds with SW
directions. A shift in the migratory direction (Gwinner and
Wiltschko, 1978) to 180 when crossing the latitude of 35 N
(points 7 and 15) increased survival by 5–19% (95% CI, paired
t, n ¼ 11) for migrants with initial SW directions but not for
migrants with initial SE directions (1% to 4%; paired t, n ¼
11). A shift in migratory directions has not been observed for
SE migrants in any empirical study.
Simulations with the more detailed wind data from 1999 to
2003, but using only the 850-mb level (point 8 and 16),
resulted in a difference in survival rates between SW and SE
migrants similar to that obtained before (95% CI: 15–48%;
two-sample t, n1 ¼ n2 ¼ 5, df ¼ 7.2). With selection for
favorable wind conditions from four altitudes, the difference
in survival rates between SE and SW migrants became very
Erni et al.
•
Wind in passerine autumn migration
737
Table 1
Summary of migration outcomes for two ‘‘populations’’ of simulated birds, one with SW and the other with SE migratory directions, under
different parameter settings
Parameter settings
SW directions
1. No windsc
2. þ Winds (850 mb)
19902000d
3. þ Stopover strategy:
selection for windsd
4. þ Threshold fuel before
crossd
5. þ Decrease orientation
error to 15 d
6. Orientation error 0 d
7. Direction shift (to 180
south of 35 N)d
8. Winds 19992003 (850
mb only)
9. Winds 19992003,
selected from four altitudese
SE directions
10. No windsc
11. þ Winds (850 mb)
19902000d
12. þ Stopover strategy: selection for windsd
13. þ Threshold fuel before
crossd
14. þ Orientation error 15 d
15. Direction shift (to 180
south of 35 N)d
16. Winds 19992003 (850
mb only)
17. Winds 19992003,
selected from four altitudese
Survival (%),
mean 6 SD
(range)
Total migration
time (days),
mean 6 SDa
Average mortality due to time, fuel, water (%)a,b
55.2 6 1.1 (5358)
30.5 6 7.7 (2044)
70.1 6 8.0
66.2 6 14.0
0
0.4
28.4
57.5
16.4
11.6
40.1 6 11.4 (2260)
56.0 6 11.2
0.1
53.8
6.1
43.3 6 11.5 (2765)
61.0 6 12.8
0.1
49.9
6.7
56.1 6 14.3 (3478)
58.2 6 11.3
0
38.9
4.9
61.9 6 13.0 (4282)
67.9 6 9.1 (5584)
55.4 6 10.5
55.3 6 10.7
0
0.01
35.5
28.6
2.6
2.4
56.7 6 13.1 (4172)
55.9 6 9.8
0
33.9
6.9
91.7 6 2.9 (8896)
51.6 6 8.1
0
4.7
3.5
5.7 6 0.4 (56)
69.9 6 7.4 (5881)
57.5 6 6.8
49.4 6 9.9
0
0.01
93.7
22.4
0.6
7.0
79.2 6 6.3 (6989)
38.0 6 7.7
0
16.4
3.9
81.7 6 7.5 (6991)
45.5 6 9.9
0.02
13.2
4.4
91.4 6 4.9 (8497)
92.7 6 6.1 (7997)
42.5 6 9.3
43.0 6 9.2
0.01
0
6.3
6.4
2.0
0.5
88.2 6 9.2 (7294)
41.1 6 9.9
0
9.8
1.2
95.5 6 2.4 (9298)
36.2 6 7.4
0
4.2
0.3
a
Means and standard deviations weighted by survival rate of each year.
Mortality due to time: exceeded time limit; fuel: out of fuel mostly over Sahara; water: out of fuel over water, including Atlantic Ocean.
c
Mean values based on 10 simulations (10 replications with same settings), stopover strategy: stop for 10 days and fly 2 nights, orientation
error ¼ 30 .
d
Mean values based on 11 simulations, each for a different year with wind data (1990–2000).
e
Birds select altitude with largest wind profit.
‘‘þ’’ indicates a feature added to the previous setting and retained in the remaining simulations. Settings 6–9 and 15–17 involve single changes
in settings from settings 5 and 14, respectively. A total of 2500 individuals per simulation.
b
small (0.1% to 7.7%; two-sample t, n1 ¼ n2 ¼ 5, df ¼ 7.7;
points 9 and 17).
Endogenous directions and wind
To examine the influence of wind on migration with different
endogenous directions, we compared four different setups.
(1) No refueling was possible in NW Africa (refueling rates set
to 0 g/day/lbm), but birds refueled to a threshold fuel level of
0.9 (90% of lean body mass) before crossing water. Only winds
at 850 mb were included, that is, there was no wind selection
across altitudes (Figure 4, open circles). Mean survival rates
were highest for SE directions (91.6 6 7.0% for directions
165–175 , overall mean 6 SD) and decreased continuously
with more westerly directions to less than 20%. (2) Refueling
in NW Africa was possible, where birds refueled to a threshold
fuel level of 0.9 for crossing the desert and 0.5 before crossing
water, and again only winds at 850 mb were included (Figure 4,
filled circles). Under these changed assumptions, survival
rates along the SW route increased (paired t 95% CI: 28–34%;
n ¼ 11) for directions between 200 and 210 , from an
average of 25.5 6 11.1% to 56.5 6 14.3%, but were still lower
than those for SE migrants with directions between 165 and
175 (32% to 38%, two-sample t 95% CI, n1 ¼ n2 ¼ 11). (3)
Birds refueled as in (1) but additionally selected for the best
wind from four altitudes (Figure 5, open circles). Survival
rates for SE migrants increased slightly from those without
wind selection over altitudes (cf. Figures 4 and 5) (two-sample
t 95% CI for directions 165–175 : 2–6%, n1 ¼ 55, n2 ¼ 121,
df ¼ 171.9; mean 6 SD: 95.6 6 3.5%). For a narrow range
of SW directions, survival rates were considerably higher
(approximately 45%) in all years than without altitude
selection, reaching a maximum mean survival rate of 66.1 6
8.0% at 210 (mean 6 SD over 5 years). (4) Refueling in NW
Behavioral Ecology
738
Figure 4
Survival rates of simulated birds with different mean flight directions
(endogenous directions). Circles represent the mean, lines the
minimum and maximum survival rate over 11 simulations, each with
wind data from a different year (1990–2000). Two strategies are
presented. (1) Open circles and dashed lines: no refueling
opportunity in NW Africa, threshold fuel level for crossing water or
desert was 0.9. (2) Filled circles and continuous lines: fuel deposition
rates in NW Africa ¼ 0.03 g/day/lbm, threshold fuel level for crossing
water ¼ 0.5 and for crossing desert ¼ 0.9. A total of 2000 birds in each
simulation (per endogenous direction and year). Orientation error
was set to 15 (angular deviation). Winds from 850 mb pressure level.
Africa was possible; birds refueled as in (2) and selected for
the best wind from four altitudes (Figure 5, filled circles).
Refueling opportunities in NW Africa again lead to a considerable improvement for SW migrants, with survival rates
considerably higher (two-sample t 95% CI for change in
survival rates: 40–49%, directions 203–208 , n1 ¼ n2 ¼ 30, df ¼
33.7; mean 6 SD: 92.7 6 3.5%) than without refueling in NW
Africa. These latter survival rates were very close to those for
SE directions from situation (3) (two-sample t 95% CI for
difference: 1–4%, n1 ¼ 55, n2 ¼ 30, df ¼ 60.8). The range of
feasible values for SW migrations was fairly narrow with
average survival rates dropping to less than 80% for
endogenous directions west of 209 and east of 196 .
DISCUSSION
Wind is known to play an important role during flight and
consequently for the migration of birds (Alerstam, 1979;
Gauthreaux, 1980; Liechti, 1995; Liechti and Bruderer, 1998;
Williams TC and Williams JM, 1990). It has, however, been
difficult to quantify the effect of wind during migration (but
see Stoddard et al., 1983). We used a computer simulation to
quantify the influence of wind on small passerine birds on
their first autumn migration from northern Europe to Africa.
Southwestern flyway
In Europe, winds from the west dominate (Figure 1).
Variability increases toward the south, mainly in the area
of the western Mediterranean Sea (Figure 2). Along the
southwestern flyway birds are frequently confronted with
cold fronts moving in from the Atlantic Ocean, causing the
Figure 5
Survival rates of simulated birds with different mean flight directions,
and wind data from the years 1999–2003. Circles represent the mean,
lines the minimum and maximum survival rates during the 5 years
with wind data (1999–2003). The simulated birds selected the altitude
with the most favorable wind from four pressure levels: 1000, 925, 850,
700 mb. (1) Open circles and dashed lines: no refueling opportunity
in NW Africa, threshold fuel level for crossing water or desert was 0.9.
(2) Filled circles and continuous lines: fuel deposition rates in NW
Africa ¼ 0.03 g/day/lbm, threshold fuel level for crossing water ¼ 0.5
and for crossing desert ¼ 0.9. A total of 2000 birds were simulated in
each simulation (per endogenous direction and year). Orientation
error was 15 .
well-known variations in migratory intensities in the temperate regions (Erni et al., 2002a; Richardson, 1990), and also
variable winds over NW Africa. Over western Europe most
passerine long-distance migrants fly with SW directions
(Bruderer and Liechti, 1999), shifting to more southerly directions over the African continent (Liechti F and Bruderer B,
unpublished data). Our results suggest that the overall
increase in survival attained by avoiding days with unfavorable
winds is relatively small; additional selection of altitudes with
favorable winds seems a necessity. On the SW flyway, survival
rates in our simulations approached those of the SE flyway
only under the additional condition that refueling was
possible in NW Africa. Various reports confirm that passerines
refuel in NW Africa during autumn migration (Bairlein, 1987,
1988; Schaub and Jenni, 2000). Our recent observations in
Mauritania indicate that refueling possibilities may be available over larger areas than previously assumed.
Southeastern flyway
Further to the east, from Greece to Egypt, northerly winds
favorable for autumn migration are common, at least below
2000 m a.s.l.. On the SE flyway migrants concentrate along the
eastern Mediterranean Sea (Bruderer and Liechti, 1999). In
addition, there are large numbers of nocturnal migrants
crossing the southern Balkan area, heading mainly southward,
indicating that they cross the corresponding parts of the eastern
Mediterranean Sea on a broad front (Zehtindjiev and Liechti,
2003). Our simulations indicate that wind conditions along the
SE route are very favorable, to the extent that birds that crossed
the Mediterranean Sea and the Sahara with a fuel load of 0.5
Erni et al.
•
Wind in passerine autumn migration
(amount of fuel relative to lean body mass) were almost as
successful as birds that crossed with a fuel load of 0.9. Without
wind assistance on the other hand, that is, only self-powered
flight, average survival during autumn migration with SE
directions would be less than 10%, as estimated by our
simulation results. This suppports Biebach (1992), who suggested that, according to fuel loads he measured on the ground,
garden warblers would not be able to cross the desert without
wind support. Thus, we conclude that this route, with a larger
sea and desert crossing than along the SW route, relies on
consistent wind support.
Southerly directions
With the environmental factors and behavioral settings
considered in this simulation study, the SW and SE flyway
seem to yield an approximately equal chance of survival
during autumn migration. For southerly directions, however,
our simulation results indicated lower survival rates. These
lower survival rates are probably caused by a combination of
less favorable wind conditions along this route than further
east and fewer refueling opportunities than further west due
to the three ecological barriers Alps, Mediterranean Sea, and
Sahara. This is in line with Alerstam’s (2001) prediction that
larger fuel loads and, therefore, increased transport costs
would favor a detour around these barriers. Some exceptions
show that the direct southward connection is at least a feasible
option: the northeastern populations of the garden warbler,
S. borin, are an example of southward migration with high fat
loads, while spotted flycatchers, Muscicapa striata, from
Scandinavia and the Baltic fly southward with low fat reserves,
feeding en route in a wide variety of (even desert) habitats
(Glutz von Blotzheim and Bauer, 1991, 1993).
North America
On the North American continent, bird migration is not
exposed to the generally adverse wind conditions experienced
by SW autumn migrants over Europe. Instead, migratory
directions are generally favored by the wind patterns, and
migrants seem to efficiently exploit this situation in spring
and autumn (Gauthreaux, 1980; Gauthreaux and Belser,
1999). A small proportion of autumn migrants leave the
northeastern American coast regularly with SSE directions
given northwesterly winds after a cold front passage, and these
birds then make use of northeasterly trade winds to carry
them to the Caribbean and South America (Williams TC and
Williams JM, 1978). This is a striking example of a migration
route that has adapted to favorable wind conditions. In
spring, practically all birds follow the coast. More generally, in
North America long-distance crossing of barriers is avoided
unless favorable winds support notable crossings. Therefore,
in both European/North African and American migration
systems wind seems to play a crucial role in determining the
feasibility and maintenance of a migration route, but a much
larger combination of factors, including conditions during
spring migration and the nonbreeding season, will ultimately
determine the continuation of bird migration between
specific breeding and nonbreeding sites. The only study we
know that separately estimates survival for the migratory
period in passerines (Sillett and Holmes, 2002) gives estimates
of approximately 70% for both autumn and spring migration
in an American long-distance migrant. It is difficult to
compare this average estimate with the survival rates of this
study. Real birds can be expected to perform better than the
simulated birds in this study, for example, flying along coast
lines (Bruderer and Liechti, 1998; Fortin et al., 1999) would
reduce the number of birds getting lost over the Atlantic
739
Ocean, causing mortality over water. On the other hand, we
have not dealt with predation or severe weather conditions,
which would decrease survival rates in reality.
This paper has suggested how small passerine migrants
cope with the unfavorable conditions on the SW flyway
between northern/central Europe and tropical Africa. To
explain why the first migratory phase of these SW migrants
takes place mostly against the prevailing winds, we favor the
earlier suggested idea that birds might follow the ancient path
of range expansion to move between breeding and nonbreeding ranges (see introduction). Our simulation results
suggest that there is no heavy selective pressure to shift to
shorter migratory routes; in contrast, favorable wind conditions to the east and favorable feeding conditions to the
west seem to favor the detours against the shortest route.
Overall, the simulation results help to identify feasible
migration strategies and environmental conditions necessary
for a successful autumn migration, so providing a first
indication of the comparative success rates along a SE versus
SW migration route.
We thank Thomas Alerstam, Res Altwegg, Heiko Schmaljohann,
Andrew Bourke, and two referees for very helpful comments and
suggestions. This study was part of a project on bird migration in the
western Mediterranean supported by the Swiss National Science
Foundation, Grant No. 31-43242.95. We would like to thank the
NOAA–CIRES Climate Diagnostics Center and the National Center
for Atmospheric Research, both in Boulder, Colorado, USA, for kindly
making the wind data available and the people behind the ‘‘R
Foundation for Statistical Computing’’ for making available the
software we used to read and organize the wind data.
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