Factors influencing nest site selection, breeding
density and breeding success in the bearded vulture
(Gypaetus barbatus)
J.A. DONAZAR, F. HIRALDO and J. BUSTAMANTE*t
EstaciOn Biológica de Doñana, (CSIC), Pabellón del Peru, Avda M’ Luisa Sn, 41013 Sevilla, Spain; and
*csIRo, Division of Wildlife & Ecology, P0 Box 84 Lyneham, ACT 2602, Australia
Summary
1. We examined the nest site selection, breeding density and breeding success
in the bearded vulture Gypaetus barbatus in relation to physiography, climate,
land-use and degree of human disturbance. The study area was in the Pyrenean
Cordillera, Spain, where the largest European population of this species occurs.
Univariate analyses and Generalized Linear Models were employed.
2. Models correctly classified the 78% of the cliffs analysed (occupied by bearded
vultures, and selected at random). The probability of occupation of a cliff by bearded
vultures was directly related to the ruggedness of the topography, altitude, distance
to the nearest bearded vulture occupied nest, and distance to the nearest village.
3. Breeding density was positively correlated with altitude and ruggedness of the
topography and negatively correlated with snow precipitation. Open areas seemed
also to have positive effects, probably by increasing the availability of food, although
its effects were not separable from that of the relief, as the two factors covary.
4. Bearded vultures showed lower breeding success in areas with high potential
human disturbance (density of paved roads). The existence of abrupt and open
lands might have a positive effect on breeding success by reducing accessibility to
humans, and perhaps by increasing food availability.
Key-words: nest-site selection, breeding density, breeding success, bearded vulture,
conservation.
Introduction
The bearded vulture (Gypaetus barbatus L.) is
a cliff-nesting accipitrid vulture inhabiting Old
World mountain ranges and feeding on bones,
predominantly of medium-sized ungulates (Hiraldo
et al. 1979; Brown 1988; Brown & Plug 1990). Its
breeding distribution has been greatly reduced in
Europe since the last decades of the nineteenth
century and is restricted nowadays to the Pyrenees,
Southern Balkans and the islands of Corsica and
Crete (Hiraldo et al. 1979). The European population is estimated to be c. 120 breeding pairs (Elosegi
1989; R. Heredia, personal communication). The
biggest population is that of the Pyrenees with 72
occupied territories (52 on the Spanish side and 20
on the French side (R. Heredia, personal communication)). The species is considered as endangered
504
Present address: Nationalparkverwaltung, Doktorberg
6, 8240 Berchtesgaden, Germany.
both in Spain (ICONA 1986) and in Europe (Conseil
de l’Europe 1981), and is included in the Annex I of
the Directive 79/409/EEC as a species sensitive to
habitat alterations.
The decline of this species has been attributed to
several causes. The cooling of the climate could
have been responsible for the decline in the Alps
during the nineteenth century (Haller 1983) but
direct persecution, killing of adults and robbery of
eggs and chicks, and indirect mortality caused by
poison baiting of carnivores are the more widely
accepted causes for the decline of the species in
Europe during the twentieth century. Nowadays
these factors seem to have only a limited influence
on the population of bearded vultures, at least in
Spain (B. Heredia 1991). As a consequence, the
population in the Pyrenees has increased steadily
during the last two decades (R. Heredia 1991a).
Nowadays, mountain habitats are being transformed
with the change in human production systems.
There is a reduction of areas used for extensive
grazing of livestock and an increase in tourism.
It has been suggested that changes in the use of
mountain areas can reduce the carrying capacity of
the environment and the breeding success of the
bearded vulture (B. Heredia 1991; Terrasse 1991).
Quantitative studies of habitat selection, as a way
to predict species requirements, are frequently used
to design strategies for the conservation of ndangered species (Morris 1980; Bednarz & Dinsmore
1981; Newton, Davis & Moss 1981; Andrew &
Mosher 1982; Peterson 1986; Gonzalez, Bustamante
& Hiraldo 1992). This kind of analysis for the bearded
vulture can be useful today as there has been a
reintroduction project in the Alps since 1978, the
release of birds having started in 1986. It is expected
that this and other projects will be extended to other
mountain ranges in the continent including Spain
(FAPAS 1991; AMA—CSIC 1991). Because these
reintroduction projects are expensive (Patchlatko
1991) it is important to evaluate the suitability of an
area before the birds are released.
The present study analyses nest-site selection,
density of breeding pairs and breeding success in
relation to variables for topography, climate, food
availability and human disturbance in the population
of bearded vultures in the Spanish Pyrenees. The
aims were two. First, to identify possible relationships between variables measured and bearded
vulture distribution, density and breeding success.
Secondly, to obtain models to evalulate whether a
proposed area for reintroduction had adequate cliffs
as nesting sites for the species, and what might be
the expected productivity and breeding density in
the area. Although it is difficult to evaluate the
confidence in the values predicted by the models
once they are used in an area different from that
from which they were calculated, these models are a
step forward when compared to a subjective assessment of suitability of proposed reintroduction areas.
Study area and methods
The bearded vulture population studied is distributed all along the southern slopes of the Pyrenees
but is more dense in the area known as Central
Pyrenees. The bearded vulture population of the
Spanish Pyrenees has been studied since 1977 and
all occupied territories are known (n 52 in 1991)
(see R. Heredia 1991a).
=
DATA
Nest site selection
Thirteen variables representing physiography, landuse and degree of human disturbance were measured
on 111 cliffs with bearded vulture nests (Table 1).
These nests belonged to 37 different breeding territories. The existence or exact location of the nests
was unknown in the remaining 15 territories. Another
111 cliffs without nests were selected at random
and used to estimate the nesting habitat available
for the species. Random points were selected by
pseudo-random generation of coordinates with a
calculator, and choosing the nearest point on a cliff
without a nest. Since, in the Pyrenees, most of
the nests were found near the middle of the cliff
(R. Heredia, unpublished), we selected a similar
location (half of the cliff height) for the random
points. To avoid bias due to different breeding
densities, random sampling was stratified and the
number of random cliffs sampled on each map sheet
where the species was breeding (‘L’ series 1:50000
topographic map of Spain, each sheet covering an
area of 26712km2) was made equal to the number
of previously sampled nesting cliffs. Variables were
measured on topographic maps of the Spanish
Cartographic Service and Land-use maps of the
Spanish Ministry of Agriculture. We also used
Table 1. Variables used to characterize bearded vulture nesting cliffs and random cliffs. For randomly selected cliffs,
variables were measured from a point in the centre of the cliff
topographic irregularity index. Total number of 20-rn contour lines, cut by four 1-km lines starting from the nest in
directions N,S,E and W.
ALTITUDE: altitude of the nest above the sea-level (rn).
CLIFF: cliff height, measured as the number of 20-rn contours cut by a 50-rn line perpendicular to the cliff face at nest level.
ORIENTATION: orientation of the cliff face at the level of the nest. Orientations were scored in increasing shelter from cold
humid winds from the NW which are dominant in the area: 1 = NW, 2 = N or W, 3 = NE or SW, 4 = E or S, 5 = SE.
FOREST: extension (%) of forested areas in a 1000-m radius around the nest.
DISTANCE VILLAGE: distance to the nearest inhabited village (km).
RELIEF:
number of inhabitants in the nearest village.
kilometres of paved and unpaved roads in a 1000-m radius of the nest.
DISTANCE PAVED ROAD: shortest linear distance between the nest and the closest paved road (km).
INHABITANTS:
ICILOMETRES ROADS:
shortest linear distance between the nest and the closest road, paved or unpaved (km).
altitudinal difference between the nest and the closest paved road, measured at the point the road is
closer to the nest (rn). If the nest is lower than the road a negative value is obtained.
HEIGHT ROAD: altitudinal difference between the nest and the closest road, paved or unpaved, measured at the point is
closer to the nest (m). If the nest is lower than the road a negative value is obtained.
NEAREST NEIGHBOUR: linear distance between the nest and the closest nest of the nearest neighbour (km).
DISTANCE ROAD:
HEIGHT PAVED ROAD:
the reports on human population census of 1981
(INE 1984).
Breeding density
We used the distance of the most frequently used
nest of a breeding pair to the nearest nest belonging
to another bearded vulture pair as an inverse measure
of breeding density in the area. Nearest neighbour
distances are currently employed as evaluators of
raptor breeding density (see e.g. Newton 1979).
Nearest neighbour distances, however, are not
independent. This is particularly clear when the
distance between two pairs is counted twice, but
even when this does not occur, independence is
unlikely as density is a global measure related to the
situation of every pair. Although our analysis does
not solve completely the problem of lack of independence of nearest neighbour distances, measures
that were repeated were considered only once.
We characterized the area surrounding 28 bearded
vulture breeding pairs in the Central Pyrenees
(58% of the Spanish population). We selected this
area because it is known that there the number of
breeding pairs has remained almost stable since 1977
(R. Heredia, unpublished), and this suggests that
the population is in equilibrium with the environment and we could expect to detect the ecological
factors that limit breeding density.
For each pair we quantified the topography,
vegetation, land-use, climate, food availability and
degree of human disturbance in a 15km radius
around the most frequently used nest-site (707 km2)
(Table 2). According to Brown (1988) bearded
vultures in South Africa forage in a circular area
of 300—700 km2 around the nest, so we assumed
that the variables measured in a circle of 15km
radius would be an adequate description of the main
foraging area of a breeding pair. The values of the
variables were obtained from the same maps as for
nest-site selection, and also from the Climatic Atlas
of Spain (Instituto Nacional de MeteorologIa 1983),
livestock census from 1986 (Spanish Ministry of
Agriculture, unpublished) and Chamois (Rupicapra
rupicapra L.) census carried by the Autonomous
Communities of Navarra, Aragon and Catalufla
(unpublished).
Table 2. Variables used to characterize the main foraging areas (a circle of 15-km radius around the most frequently
used nest)
topographic irregularity index. Number of 100-rn contours cut by four 15-km lines starting from the nest in
directions N,S,E and W.
MAXIMUM ALTITUDE: maximum altitude in the main foraging area.
MINIMUM ALTITUDE: minimum altitude in the main foraging area.
AVERAGE ALTiTUDE: average altitude in the main foraging area = (maximum altitude + minimum altitude)/2.
ALTITUDINAL DIFFERENCE: maximum altitude
minimum altitude.
AREA OVER 1600 m: percentage of the main foraging area over 1600 m.
TEMpERATURE: average annual temperature.
SUNSHINE: average annual number of hours of sunshine.
RAINFALL: average annual rainfall (mm).
DAYS WITH RAIN: average annual number of days with rain.
DAYS WITH SNOW: average annual number of days with snow.
WINTER RAiNFALL: average rainfall in Decernber, January and February, during the courtship and laying period of the
bearded vulture.
SPRING RAINFALL: average rainfall in March and April, during the incubation and hatching of the bearded vulture.
CULTIVATED LANDS: percentage of the main foraging area covered by cultivated lands.
FORESTS: percentage of the main foraging area covered by forests.
PASTURE LANDS: percentage of the main foraging area covered by pasture lands.
HIGH MOUNTAIN: percentage of the main foraging area covered by unproductive high mountain terrain (rocky outcrops,
snow patches, screes).
SCRUBLAND: percentage of the main foraging area covered by scrubland.
OPEN LAND: sum of pasture lands, high mountain and scrubland.
DISTANCE TO CAPITAL: linear distance from the nest to the nearest provincial capital.
VILLAGES: number of permanently inhabited villages in the main foraging area.
VILLAGES OVER 1000 INHABITANTs: number of villages with more than 1000 inhabitants in the main foraging area.
RELIEF:
—
V
INHABITANTS:
total number of inhabitants in the main foraging area.
KILOMETRES OF PAVED ROADS: kilometres of paved roads in the main foraging area.
KIL0METRES OF UNPAVED ROADS: kilometres of unpaved roads in the main foraging area.
TouRIsM: number of hotel beds and camping places in the main foraging area.
KILOMETRES OF ELECTRIC POWER LINES: kilometres of high tension electric power lines in the main foraging area.
LIVESTOCK: number of sheep and goat per km2 in the main foraging area. We assumed that the number of sheep and goat
from a municipality in a main foraging area was proportional to the percentage of that municipality inside the main
foraging area.
CHAMoIS: number of chamois (Rupicapra
TOTAL UNGULATES: livestock + chamois.
rupicapra) per km2 in the main foraging area.
To evaluate breeding success we used the average
productivity of each breeding pair (defined as:
number of fledglings raised per number of years
monitored). Productivity values were available for
25 breeding pairs from all the Spanish Pyrenees.
These pairs were monitored for more than 5 years
(mean 1O2 years, SD 38). Productivity values
were compared with the variables characterizing the
nest-site (the most frequently used nest of each pair)
and the main foraging area.
One appropriate link function for a binomial
distribution is the logistic function. This means that
the probability of a cliff being selected as a nest site
or of a chick fledging in a territory a certain year is a
logistic, s-shaped function when the linear predictor
is a first-order polynomial or a bell-shaped function
for second-order polynomials. The logistic function
can be expressed as:
p
=
(e’’)/(1 + eLP),
eqn 2
where p is the probability of obtaining a positive response and e is the base of the natural logarithm. This
expression can be transformed to a linear function:
eqn 3
ln[p!(1
First we made a univariate analysis of the data.
Mean values for nesting cliffs and random cliffs were
compared using t-tests. Nearest neighbour distance
of the pairs in the Central Pyrenees was compared
with the variables characterizing the main foraging
area, and productivity values were compared with
the variables characterizing the most used nest-site
and the main foraging area.
Secondly, we used Generalized Linear Models, or
GLM (Nelder & Wedderburn 1972; Dobson 1983;
McCullagh & Nelder 1983), to make a mathematical
description of the nest-site selection, breeding
density and breeding success of the bearded vulture.
Generalized Linear Models are a class of models
from which the linear regression forms a particular
case. GLM permit a wider range of relationships
between the response and the explanatory variables
and the use of other error formulations when the
normal error for the traditional regression is not
applicable.
Three components have to be defined for a GLM:
a linear predictor, an error function and a link function. A linear predictor (LP) is defined as the sum
of the effects of the predictor variables as follows,
where ln is the natural logarithm. Equations 1 and 3
define the GLM for nest-site selection and breeding
success of the bearded vulture.
To model the nearest neighbour distance, or
breeding density, we used an identity link. In these
cases the models do not differ from a multiple linear
regression with the dependent variable (nearest
neighbour distance) log-transformed.
LP
=
a + bx1 + cx2 +
eqn 1
where a, b, c,... are parameters to be estimated
from the observed data and x1, x2,... the explanatory variables. These parameters define the effect of
the variables on the LP.
The error function will depend on the nature of
the data. For binary response variables the binomial
distribution is an adequate error function. We
assumed a binomial distribution of errors in the
models of nest-site selection, in which the response
variable had the value 1 (cliff selected as a nest-site)
or 0 (cliff not selected as a nest-site), and in the
models of productivity in which the response variable
had the value 1 (when a chick had been fledged from
a territory a certain year) or 0 (when no chick had
fledged). Nearest neighbour distances seemed to
be log-normally distributed, so values were logtransformed and a normal distribution of errors was
assumed for the models.
—
p)]
LP,
STATISTiCAL ANALYSiS
ANALYTiCAL
PROCEDURE
For nest-site selection we divided each predictor
variable into six classes and graphically represented
the number and percentage of nesting cliffs in relation
to total number of cliffs in each class. Average
productivity and nearest neighbour distance for each
of the 25 and 28 breeding territories respectively
were plotted against the values of each predictor
variable. Visual inspection of these graphs revealed
which shape of response could be expected for each
predictor variable (linear or curved response) and
whether a transformation of the predictor variable
could be recommended.
We fitted each explanatory variable to the observed
data using the program GUM (Baker & Nelder
1978) following a modification of a traditional forward stepwise procedure. Each variable was tested
for significance in turn. The variable contributing to
the largest significant change in deviance from the
null model was then selected and fitted. Once a
variable was fitted to the model we tested if the
addition of a second variable significantly improved
the model. As we were using a large number of
variables we chose a 1% level of significance to
include a variable in a model.
If the initial bivariate graphs suggested a curved
response a quadratic function was tested initially,
and a cubic term was then tested to ensure that
a higher order polynomial was not necessary to
improve the model. Square root and logarithmic
transformation of all the predictor variables involving distances were also tested as the graph suggested
they had more a multiplicative than an additive
effect.
Variable
es
Nesting cliffs
Random cliffs
Student’s
Table 3. Mean (SD) of the variables characterizing nesting and random cliffs
83 (180)
1333 (361•2)
1289 (378)
3•13 (127)
16787 (11154)
308 (2.40)
175 (2587)
66 (154)
1325 (5264)
1021 (323)
303 (121)
15541 (10266)
263 (201)
112 (2062)
HEIGHT PAVED ROAD
151 (2.21)
2•06 (158)
102 (O88)
40469 (252.29)
2•42 (295)
234 (171)
1.05 (1.05)
39063 (33027)
HEIGHT ROAD
25081 (29097)
25405 (31072)
810 (600)
RELIEF
ALTITUDE
CLIFF
ORIENTATION
FOREST
DISTANCE VILLAGE
INHABITANTS
RILOMETRES ROADS
DISTANCE PAVED ROAD
DISTANCE ROAD
NEAREST NEIGHBOUR
*
P<005,
11.10 (544)
7.813***
0132
5.679***
0601
0866
1509
2.005*
2.609*
1295
0•192
0356
0080
3.896***
P<0•0O1.
Recent papers have criticized automatic stepwise
procedures as they are not necessarily able to select
the most influential variable from a subset of variables (James & McCulloch 1990). Our modification
of a stepwise modelling procedure involved testing
the alternative models that were obtained when the
second or the third most significant variable was
included (provided it was significant at the 1% level)
instead of the first most significant one at each of
the steps. This branching procedure could eventually
produce a set of different models, but in most instances it converged into a single model or to a set
of models from which similar causal relationships
could be inferred.
In addition, a residual analysis was undertaken
for the best model or a set of best models. Three
diagnostic measures were used to evaluate the fit of
the models to the data: a measure of the residual
lack of fit, the potential influence, and the coefficient
of sensitivity of each observation (Pregibon 1981;
Nicholls 1989). Standardized residuals were plotted
against fitted value for possible deviations of the
initial assumptions of the model. Observations
with high potential influence were re-examined
looking for errors in the data or possible outliers.
Observations with a high coefficient of sensitivity
were excluded and models refitted evaluating the
effect these observations had on the parameters
of the models.
The robustness of the nest-site selection model
was also tested with a jack-knife procedure. Each
observation was omitted in turn and the parameters
of the models were recalculated with the remaining
observations. We obtained from this model the
probability the excluded observation had of being a
nesting cliff. Percentages of correct classification
obtained by the jack-knife procedure were then
compared to those of the initial model.
Results
NE5T
SITE
SELECTiON
An average of three nests per breeding territory was
known in the 37 territories with known nesting sites
in the Spanish Pyrenees (range 1—6 nests, SD = 3).
The average distance from a nest to the nearest nest
in the same territory was 992m (range 50—8450,
SD = 1443, n = 104) and to the farthest nest 2696m
(range 50—10 150, SD = 2467, n = 104).
There were significant differences between nesting
cliffs and random cliffs in variables related to relief
and cliff height. Nests tended to be located in areas
more rugged and at higher cliffs than the average
available. Also, nesting cliffs tended to be farther
from the nearest occupied nest of bearded vulture
than from random cliffs (Table 3).
Observations of nesting cliffs (1) and random
cliffs (0) were fitted to a GEM model assuming a
binomial distribution of errors and using a logistic
link (equivalent to a logistic regression).
The best model obtained (the one with smaller
residual deviance) included the variables: relief, distance to the nearest occupied nest (log-transformed),
altitude (quadratic function) and distance to the
nearest village (log-transformed) (Table 4). It showed
that the bearded vultures selected as nesting cliffs
those in areas with the most irregular topography,
far from other breeding pairs, at an average altitude
(avoiding cliffs at high or low altitudes) and not
close to villages.
Other alternative models only differed in the
order in which the different explanatory variables
were included and finally converged with this same
model. Cliff heights (which had a highly significant
correlation with topography, r = 0674, df = 220,
P < 0001) were included in some of the initial
models but they had a bigger residual deviance than
those which included relief instead, and cliff height
did not significantly improve the model once relief
Table 4. GLM model for nest-site selection, using binomial
error and logistic link. Distance to the nearest neighbour
a
(negative correlation) and altitudinal
difference
(negative correlation) in the main foraging range
(Table 5).
nd distance to village are log-transformed
J
Parameter estimate Standard error
Constant
RELIEF
NEAREST NEIGHBOUR
—33•93
009058
1644
Residual deviance
0009867
—4024 x 10
0945l
190•12
df
216
ALTITUDE
(ALTITUDE)2
DISTANCE VILLAGE
5452
001480
03405
0003l74
ll46 x 10
O•2917
had been included. The log-transformation of distance to the nearest occupied nest and distance to
the nearest village significantly improved the models
compared with the untransformed variables. The
coefficients of these variables show that distance to
a village is only important when cliffs are very close
to villages (coefficient <1), but distance to the
nearest occupied nest is important over the range of
distances measured (almost all cliffs in a 8-km radius
of a breeding pair are unavailable for other pairs)
indicating that nests tend to be regularly spaced
in the area.
Fitted values of the GLM models can be interpreted as the estimated probability (P) of a cliff
being a nesting cliff. Those observations with P> 05
were considered classified as nesting cliffs and
those with P < 05 as random cliffs. The final model
classified correctly 793% of the nesting cliffs and
76.6% of the random cliffs. This classification is
56% better than random (Kappa = 0559, Z = 8337,
P <0.001). The jack-knife classification showed
the robustness of the model; 78.4% of the nesting
cliffs and 757% of the random cliffs were correctly
classified. On average, the jack-knife procedure
only misclassified 1% more observations than the
complete model.
The sensitivity analysis of the model did not show
any outliers. All observations with high potential
influence in the model were checked and data values
were found to be correct and reasonable. The observation with the highest coefficient of sensitivity was
omitted and parameters refitted. The change in the
parameters was less than 4%.
Nearest neighbour distance (an inverse measure
of breeding density) was log-transformed and a
GLM model with an identity link and assuming
normal error, was fitted (equivalent to a multiple
linear regression).
The stepwise branching procedure produced
three similarly significant models. Each included
one of three variables related to altitude (maximum
altitude, altitudinal difference, average altitude, all
significantly correlated P < 0.001) and number of
days with snow as a second variable (Table 6). All
models indicated that density of breeding pairs
increased with altitude which means that within the
range of altitudes at which nests were found, the
distance between nests is greater at higher altitudes.
All the models showed also that breeding density
decreased with the number of days with snow.
The extension of open areas in the foraging range (a
variable positively correlated with altitude, r = 0825,
df=26, P<0001) was the first variable to come
into the model, but no other variable significantly
improved this model (although number of days
Table 5. Correlation between nearest neighbour distance
and variables characteriring the main foraging area in the
Central Pyrenees (df 26). *Correlations that remain
significant (P < 0.05) after Bonferroni sequential correction
(Rice 1989)
=
r
P
—0•473
—0514
—0036
—0465
—0565
—0384
0.011
0005
0856
00l3
0.002*
0044
0193
0325
0198
—0•258
—0•246
—0000
—0340
—0418
0268
0.382
—0•179
—0500
—0172
—0585
—0229
0063
0•392
0311
0185
0.207
0077
0027
0•168
0045
0361
0007
0383
0.001*
0240
0751
0039
INHABITANTS
0066
0737
KILOMETRES PAVED ROADS
0274
0158
—0211
—0139
0280
0432
0244
0199
—0394
0.101
0212
0.311
0038
0608
Variable
RELIEF
MAXIMUM ALTITUDE
MINIMUM ALTITUDE
AVERAGE ALTITUDE
ALTITUDINAL DIFFERENCE
AREA OVER
1600m
TEMPERATURE
SUNSHINE
RAINFALL
DAYS WITH RAIN
DAYS WITH SNOW
WINTER RAINFALL
SPRING RAINFALL
CULTIVATED LANDS
FORESTS
PASTURE LANDS
HIGH MOUNTAIN
SCRUBLAND
BREEDING
DENSITY
OPEN LAND
DISTANCE TO CAPITAL
The average distance to the nearest neighbour in the
Spanish Pyrenees was 110Mm (range = 2125—28000,
n = 51). In the core area selected for the study of
breeding density, the Central Pyrenees, the corresponding estimate was 8813m (range 2125—19500,
n = 28).
There were significant correlations between
nearest neighbour distance and the extent of open
areas (pastures, scrubland and high mountain)
VILLAGES
VILLAGES OVER
1000 INHABITANTS
KILOMETRES UNPAVED ROADS
TOURISM
KILOMETRES ELECTRIC POWER LINES
LIVESTOCK
CHAMOIS
TOTAL UNGULATES
0999
Table 6. GLM models for nearest neighbour distance (log-transformed), using normal error and identity link
Parameter estimate
Standard error
constant
MAXIMUM ALTITUDE
0-1982
1-154 x iO
0-006570
9-963
—8-025 x io—
0-03458
2-1987
DAYS WiTH SNOW
Residual SS
df
F(2,25)
25
Constant
23-67 (P<0-01)
ALTITUDINAL DIFFERENCE
9-681
DAYS WITH SNOW
0-1703
io
—8-022 x
Residual SS
df
F (2,25)
1-156 x 1o4
0-02782
2-2033
0-005909
25
Constant
23-67 (P<0-01)
AVERAGE ALTITUDL
10-20
—0-00153
DAYS WITH SNOW
Residual SS
df
F (2,25)
io—
0-007448
0-04005
2-3330
25
22-89 (P<0-01)
Null Model SS 6-4580 df
=
=
27.
With SnOW Was nearly significant, 001 <P < 005).
Although the reduction in deviance by extension of
open areas was Slightly greater, it wa not significantly different from the variables related With altitude. Once number of days With snOw was included
in the model the inclusion of an altitudinal variable
improved the model more than the inclusion of
extension of open areas. The altitudinal variable of
the model could also be substituted by relief.
Inspection of the residuals showed that the logtransformation of the nearest neighbour distance,
the normal error and the identity link, were reasonable assumptions for the model. No outliers were
detected from the potential influence measure.
Removal of the observation with the highest coefficient of sensitivity produced a 12% change in the
parameters, which seemed reasonable given the
number of observations (n 28).
=
BREEDING
0-2343
2-339 x
SUCCESS
Of the variables characterizing the nesting cliff,
productivity was significantly correlated only with
the relief (Table 7). Pairs nesting in cliffs in more
rugged terrain had higher productivity. Of the variables characterizing the main foraging area, number
of inhabitants and kilometres of paved roads were
significantly negatively correlated with productivity
while the extent of open areas (pastures, scrubland
was significantly positively
and high mountain)
correlated.
We considered the number of chicks fledged in a
territory (a maximum of one chick is fledged per
year) as a variable with a binomial distribution, and
use it as binomial denominator for the models in
which breeding success of the territory was known
for a number of years.
The best model included only the variable ‘kilo­
metres of paved roads’ in the main foraging area,
and showed that breeding success was lower in areas
with a high density of paved roads (Table 8). The
residual deviance of the model was still quite large
but no other variable significantly improved it.
Other variables (extent of open land, number of
inhabitants, relief of the main foraging area, relief
of the nesting cliff, maximum altitude in the main
foraging area, and nesting cliff height) also significantly decreased the deviance in relation to the null
model. All these variables were significantly correlated with kilometers of paved roads, but models
with these variables still had a significant (P <001)
or nearly significant (P <0.05) decrease in deviance
when kilometres of paved roads was included.
The sensitivity analysis pointed out some territories with high potential influence. These corresponded to territories with valus close to the
maximum and minimum of the variables (number of
chicks fledged, and kilometres of paved roads), and
with a high binomial denominator (number of years
the breeding success was known). There were no
reasons to consider them as outliers. Omitting the
pair with the highest coefficient of sensitivity in the
model produced a 10% change in the coefficient of
kilometres of paved roads.
Discussion
NEST
SITE
SELECTION
The results show that the bearded vulture has a
strong selection for certain nesting cliffs from those
available. The 78% correct classification of cliffs by
the GLM model can be considered good, given that
Table 7. Correlation between productivity (Average
number of fledglings per year) and variables characterizing
nesting cliff and the main foraging area (df = 23).
*Correlations that remain significant (P < 005) after
Bonferroni sequential correction (Rice 1989)
P
Nesting cliff
0561
RELIEF
0323
0518
—0251
0042
0219
0•029
—0135
—0247
ALTITUDE
CLIFF
ORIENTATION
FOREST
DISTANCE VILLAGE
INHABITANTS
KILOMETRES ROAOS
DISTANCE PAVED ROAD
0• 003*
O•115
0008
0•226
0843
0292
0889
O•519
0233
DISTANCE ROAD
0121
0564
HEIGHT PAVED ROAO
0210
0313
HEIGHT ROAD
0340
—0312
NEAREST NEIGHBOUR
Main foraging area
RELIEF
0•487
MAXIMUM ALTITUDE
0501
MINIMUM ALTITUDE
AVERAGE ALTITUDE
ALTITUDINAL DIFFERENCE
AREA OVER
1600m
TEMPERATURE
0•305
0485
0•509
0452
—0140
—0146
0149
SUNSHINE
RAINFALL
WINTER RAINFALL
0042
0t93
0•090
SPRING RAINFALL
0193
DAYS WITH RAIN
DAYS WITH SNOW
—0304
—0430
0385
0384
0178
0•594
0273
CULTIVATED LANDS
FORESTS
PASTURE LANDS
HIGH MOUNTAIN
SCRUBLAND
OPEN LAND
DISTANCE TO CAPITAL
VILLAGES
VILLAGES OVER
1000 INHABITANTS
INHABITANTS
KILOMETRES PAVED ROADS
KILOMETRES UNPAVED ROADS
TOURISM
KILOMETRES ELECTRIC PUWER LINES
LIVESTOCR
—0113
—0•391
—0.638
—0•623
—0008
0•096
—0219
—0203
OO14
0.011
0138
0•014
O•023
O•504
0•487
0477
O•668
O356
0•139
0032
0•393
0.002*
0186
059t
0.001*
O•969
0•648
0193
0369
CHAMOIS
—0146
TOTAL UNGULATES
0•486
it can be expected that not all the cliffs adequate for
the species will be used.
The model Shows that relief, distance to the
nearest nest of a neighbouring pair, altitude and
Table 8. GLM model
distance to villages are, in this order, the main
factors conditioning the selection of a cliff as a
nesting site. Relief can be considered the main
factor, as it alone classifies correctly 69% of the
cliffs. There are three reasons why bearded vultures
should have preference for areas of rugged topography to locate their nests.
1. In rugged areas slope winds are a frequent
phenomenon (Pennycuick 1972), and these are
frequently and efficiently used by the bearded
vulture (Hiraldo et al. 1979; Brown 1988). This
will probably facilitate food search in the adverse
weather which is common in mountain areas. Brown
(1988) found that in South Africa radlo-tagged
bearded vultures used escarpments during their
movements more than expected.
2. In rugged areas it is easier to find slopes with
rocky outcrops exposed to the wind and free of
snow that can be used as ossuaries (Boudoint 1976).
Ossuaries are usually located close to the nests,
and are important for the species to facilitate the
breaking of bones (Boudoint 1976; Brown 1988)
and as stores of food (R. Heredia 1991b).
3. Rugged terrain near the nesting cliff will make
human access to the area more difficult; furthermore
cliffs on rugged areas are on average higher than
other cliffs. This will tend to reduce human disturbance during reproduction.
Distance to the nearest occupied nest as a factor
conditioning selection of the nesting cliff suggests a
limited availability of cliffs suitable for nesting, and
thus the need for active defence of such nest cliffs
within the home range of a territorial pair. Bearded
vultures accept other conspecifie searching for food
in their foraging area because their presence is
beneficial in the location of carcasses (Brown 1988).
Brown suggested that the actual density in South
Africa is not limited by territorial behaviour or by
availability of nesting sites, but the situation in
the Pyrenees could be different. Eighty per cent of
bearded vulture nests are in caves on cliffs (Hiraldo
et al. 1979; Canut et al. 1987). As the availability of
caves is limited in South Africa this determines the
distance between alternative nests of a breeding pair
(Brown 1988; Brown, Brown & Guy 1988). In the
Pyrenees, distance between alternative nests of a
breeding pair is on average four times greater than
in 74 pairs studied in South Africa (922 m vs. 230 m),
although nearest neighbour distance between pairs
is smaller (11 lOOm vs. 15300m). Breeding pairs in
for breeding success (probability of rearing a
chick to fledging) using binomial error and a logistic link
Parameter estimate
Constant
KILOMETRE5 PAVED ROADS
Residual deviance
df
3436
—001861
33572
Standard error
05400
0004006
23
the Pyrenees have on average three alternative nestsites (range 1—6, SD 3, n 37 pairs) and a low
rate of reoccupancy of the same nest in consecutive
years (29.1%, R. Heredia, personal communication).
The use of alternative nests would permit the avoidance of parasite infestation by consecutive use
(Newton 1979). All this suggests that adequate
nesting sites in the Pyrenees are a more limiting
factor than in other parts of the species’ distribution
area; each pair has a number of nests which are
utilized alternatively and they could be defended
from other neighbouring pairs over a great part of
the home range.
A bell-shaped response to altitude with a maximum
at 1230 m indicates that bearded vultures avoid
cliffs at low altitude, probably because a greater
extension of a circular area around the nest site will
be forested and not adequate for foraging and,
moreover, more intensively used by man. Cliffs at
high altitude are avoided, probably because of
greater exposure to inclement weather and because
of the energetic disadvantage of having the nest
high in relation to the foraging area (Bergier &
Cheylan 1980).
Avoidance of cliffs which are near villages clearly
points to human disturbance as a limiting factor for
the bearded vulture. Similar tendencies have been
found in other large raptors (Newton 1979; Andrew
& Mosher 1982; Donázar, Ceballos & Fernández
1989) and may be the result of the intense persecution that this and other species have suffered
during the last century (Bijleveld 1974; Hiraldo
et al. 1979).
=
BREEDING
=
DENSITY
The results are not as clear as those for nest-site
selection, probably because of the limited sample.
The models suggest an increase in breeding density
with altitude (within the range selected by the species)
and ruggedness of the topography, and a decrease
in density in those areas with more snowfall.
The altitude and ruggedness of the topography
probably influence the existence of adequate breeding
places. As cliff availability seems limited (see above)
breeding density would be favoured by the uneven
terrain. A similar trend was found by Ceballos &
Donázar (1989) in a population of Egyptian vultures
(Neophron percnopterus): the breeding density
was directly related to the availability of cliffs. In
other raptor species the availability of food has an
important role in the regulation of the breeding
densities (Newton 1979). It is difficult to assess the
influence of this factor in the case of the Pyrenean
bearded vultures. Open areas, whose extent is correlated with altitude and with breeding density, are
used in the Pyrenees by wild and domestic ungulates
(Dendaletche 1973). Density of chamois is positively
correlated with this variable (r=0.781, df=26,
P <0.0001) but not density of livestock (r —0517,
df 26, P 0.005). The lack of positive correlation
with density of livestock could be due to an erroneous
estimate of density of livestock in the main foraging
areas around bearded vulture nests. In the Pyrenees,
the livestock has a clumped distribution in favourable areas and has seasonal altitudinal movements
(Elosegi 1989). All these spatial variations could not
be detected in our estimates, as the livestock census
only gives numbers per municipality. Also, the
location of carcasses is easier over open areas. It
can be expected, therefore, that the extent of open
areas provides a better estimate of food availability
than density of ungulates. Ruggedness of the terrain,
also correlated with altitude, means greater availability of nesting cliffs and of rocky outcrops that
can be used for ossuaries and, in general, a topography more suitable for the flying behaviour of
the bearded vulture.
Snowfall could also affect food availability in winter
and early spring because areas with higher snowfall
will have a greater percentage of the ground covered
by snow reducing the availability of carcasses and
forcing the birds to forage over a wider area.
=
=
=
BREEDING
SUCCESS
Several variables are significantly correlated with
productivity but also with each other. The bearded
vulture shows lower productivity in areas with a
high density of paved roads in the main foraging
area, but these are also areas at lower altitude, more
densely populated, with smaller extension of open
terrain and more uniform relief. The modelling
points out that density of paved roads is the best
predictor, suggesting that human disturbance may
be the main factor limiting the bearded vulture
productivity in the area. Other factors like food
availability, represented by extent of open areas,
might also have an influence on productivity.
It could be expected that similar factors were
affecting both breeding success and breeding density
(Newton 1979). However, our results show that
altitude and number of days with snow are the main
factors limiting breeding density, while human
disturbance most affects breeding success. This
apparent contradiction may be due to the recent
human use of the mountain. In species with a long
generation time like the bearded vulture the strong
habitat selection may change slowly with temporary
decreases in the breeding success provoked by
human disturbance. In fact, although some bearded
vulture pairs suffer egg or young losses every year
due to human activities, they do not desert their
breeding areas (R. Heredia 1991a).
Conclusion
In the Pyrenees, the probability of a cliff being
513
J.A. Dondzar,
F. Hiraldo &
J. Bustamante
selected as a nesting site by the bearded vulture
increases if it is located in rugged areas, far from the
nests of other breeding pairs, far from villages, and
reaches an optimum at an altitude of 1226m. The
equation that gives the probability (P) of a cliff
being selected is:
In general, the predictions of our models will be
more reliable in areas with a similar range of variables to that of the Pyrenees, and we think they
could be applied to other mountainous areas in the
centre and south of Europe.
p) = —3393 + 009058 RELIEF + 1•644
4•024
+ 0•009867 ALTITUDE
10_6 ALTITUDE2 + 09451 DISTANCE VILLAGE.
Acknowledgements
Ln (p/i
—
NEAREST NEIGHBOUR
—
The breeding density that an area can maintain
seems to increase with ruggedness and altitude
(within the range selected by the species) but decreases in areas of high snow precipitation. One
of the models that gives the nearest neighbour
distance is:
Ln
NEAREST NEIGHBOUR
MAXIMUM ALTITUDE
=
9963
—
8•025 i0
+ 0•03458 DAYS
WITH SNOW.
The potential productivity of a breeding territory
depends on the degree of human disturbance and
in our model is inversely related to kilometres of
paved roads in a circle of 15km radius around the
nest. The model that predicts the probability of
successfully rearing a young is:
Ln (p/i
—
p)
=
3436
—
001861
We are indebted to R. Heredia for unpublished
data, Y. Menor de Gaspar for part of the habitat
measurements, and Drs A.O. Nicholls, M. Ferrer
and 0. Ceballos for their constructive comments on
the manuscript. Generalized Linear Models were
fitted with GLIM at CSIRO Division of Wildlife &
Ecology (Australia). This study was supported by
the AMA—ECC project for regeneration of habitats
of endangered species in the Natural Park of the
Sierras de Cazorla, Segura y Las Villas.
KILOMETRES
PAVED ROADS.
The applicability to other areas of the models
developed here will depend on the similarity of
these areas to the Pyrenees, and to the extent that
the variables used in our models determine the
breeding distribution of the species. The breeding
habitat (mountains), the nesting sites (cliffs), the
diet (bones) and the absence of predators and
competitors are similar in its whole area of distribution (Hiraldo et a!. 1979). This suggests that the
factors affecting the selection of a cliff as a nesting
site, the breeding density and the productivity in the
Pyrenees may be similar in other areas of its present
or former distribution.
Our models might not be applicable in areas
where the food for the bearded vulture is limited.
Other studies (Canut et a!. 1987) have shown that
an excess of food is available in the Pyrenees for
the bearded vulture population. Before using our
models in another area it would be necessary to
estimate whether the food available is sufficient for
a certain population size. The food supply necessary
for a breeding pair of the European subspecies
G.b. barbatus has been estimated as 350 kg of bones
and meat per year (Hiraldo et a!. 1979); Brown
(1988) calculated a yearly food requirement of
around 300 kg for a breeding pair of the smaller
African subspecies Gb. meridionalis. Our productivity model might also be unsuitable for underdeveloped areas in which kilometres of paved roads
may not be a good indicator of the degree of human
disturbance.
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