Age-related environmental sensitivity and weather mediated nestling
mortality in white storks Ciconia ciconia
Roger Jovani and José L. Tella
.
We studied environmental sensitivity and mortality related to weather inclemency in
white stork nestlings Ciconia ciconia in their southern European boundary (Doñ ana,
SW Spain). The study of homeothermy acquisition and fault bars (i.e. a measure of
stress on feathers) revealed that stork nestlings were specially sensitive to environmental
conditions occurring before 20 d of age. Accordingly, most of nestling mortality
concentrated during this sensitive period: 91% of deaths corresponded to nestlings
younger than 20 d, 73% concentrating on nestlings up to 10 d-old. Nestling mortality
and total breeding failure were highly variable among years, being especially high when
rainy periods coincided with the early live of nestlings (between 1 April and 15 May).
Maximum temperatures had a positive correlation with breeding success and nestling
survival but this effect disappeared when controlling for rainfall. Our results are in
agreement with previous studies conducted in other white stork populations in other
latitudes. We suggest that this could be the result of a low homeothermy capacity of
young nestlings jointly with an early breeding phenology that expose white storks to
rain, but not to high temperatures. In the context of global climate change we suggest
that the current decrease on spring rainfall could increase nestling survival while
punctual rainy springs could have a negative effect on the reproduction of white storks.
R. Jovani (jovani@ebd.csic.es) and J. L. Tella, Dept of Applied Biology, Estación
Biológica de Doñana, C.S.I.C. Avda. Ma Luisa s/n, E-41013 Sevilla, Spain.
Weather governs fundamental processes of ecosystems,
ultimately shaping the abundance and distribution of
species (Jenouvrier et al. 2003, Wichmann et al. 2003).
Thus, global climate change seriously compromise
current state of nature, being of the major concern in
conservation biology (McCarty 2001, Parmesan and
Yohe 2003).
Global climate change is now known to be affecting
bird populations (Nott et al. 2002), but this effect differs
among bird species (e.g. migration phenology, Cotton
2003), and even among populations of a given bird
species (e.g. laying date, Eeva et al. 2002, Sanz et al.
2003). In the same manner, weather is an important
regulator factor of the breeding performance of birds
(Moss et al. 2001), but even for the same bird species the
effect of global climate index as well as local weather
variables could differ among localities (Sæther et al.
2003). For instance, Redpath et al. (2002) found a
negative effect of low temperatures in nestling survival
of Scottish populations of hen harriers Circus cyaneus ,
but a negative effect of high temperatures on the
breeding success of Spanish populations.
A further point in this puzzling picture is that
although climate is changing in a global way, weather
conditions act locally upon birds. Thus, general patterns
of climate change must not necessary apply always and
everywhere. For instance, Rodrı́guez and Bustamante
(2003) found that the winter NAO index was bad on
predicting the breeding success of the lesser kestrel Falco
naumanni in south Europe, but they found that local
weather conditions acting timely upon reproduction
explained quite well the breeding success of this small
falcon.
Thus, the current knowledge of a general fingerprint
of global climate change on birds could not replace, but
rather stimulate, species-oriented studies aimed to elucidate the proximal mechanisms linking weather conditions with bird performance in space (i.e. geographic
range of species) and time (i.e. population trends).
A major issue to understand the effect of weather on
birds is the study of its effect on breeding success. Here,
we have conducted a long-term study on the effect of
weather upon the nestling stage of white storks Ciconia
ciconia in their southern European distribution range.
The white stork is a large-sized mainly migratory wading
bird that breeds from North Africa up to northern
Europe (Cramp and Simmons 1977). White storks are
semialtricial, and thus they are not homeotherms at
hatching, but gain homeothermy capacity parallel to
their increase in body mass (Tortosa and Castro 2003).
They leave the nest and the feeding by parents from 60
up to 90 d of age (Redondo et al. 1995). Since they breed
in exposed open nests, severe weather conditions could
directly affect nestling survival by increasing energy
requirements of nestlings or directly by critically lowering or elevating their body temperature.
According to the natural history of the species and the
warm climate of the study area, we formulated and
tested three hypotheses related with the possible effects
of weather upon their breeding success: a) environment
sensitivity decreases through the nestling period;
b) younger nestlings suffer of higher mortality, and
c) both rainy and very hot breeding seasons increase
nestling mortality and decrease nest fledging success.
Methods
Study area
We studied nestling performance during ten breeding
seasons (from 1981 to 2003) in a white stork colony
placed near the Doñ ana National Park (‘‘Dehesa de
Abajo’’, Puebla del Rı́o, Sevilla, SW Spain), where
storks breed at the top of wild olive trees in open nests
completely exposed to rain and sun.
where A is the asymptotic body mass of nestlings at the
end of the nestling period. From the measure of
wingcord length and body mass of 1946 nestlings from
1998 to 2003 we fitted a second order equation to
calculate the asymptotic mean weigh of nestlings at 60 d
of age (i.e. before nest departure, Redondo et al. 1995).
Fault bars are apparent discontinuities ( B2 mm)
almost perpendicular to the rachis produced during the
formation of the feather because of sliming or absence of
some barbules (Murphy et al. 1989, Prum and Williamson 2001). These feather abnormalities are produced by
an array of stressors (Slagsvold 1982, Newton 1986,
Murphy et al. 1989, Machmer et al. 1992), and have been
shown to be related with fitness components of individuals (Bortolotti et al. 2002). Moreover, they are
appropriate for the purposes of this study, because the
approximate moment of their formation could be known
by their relative position on the feather, distal fault bars
being produced at the beginning of feather growth, and
so at a younger nestling age. White stork nestlings avoid
producing fault bars on distal wing feathers, which
has been hypothesised to be an adaptation to lower
the costs of feather breakage due to fault bars in species
with high flight requirements such as the with stork
(Jovani and Blas 2004). Thus, fault bars occurring on
primary feathers of white stork nestlings could be a good
index of important stressors occurring during nestling
development.
From 196 to 51 nestlings in 2002 and 2003 respectively, we measured wingcord length and the distance
of each fault bar occurring on the fifth primary
(i.e. the longest one) to the tip of the feather.
In 2003 we also measured wing length and the portion
of the fifth primary emerging from the feather sheath
from 65 nestlings at different growing stages. Thanks
to the close relationship between fifth primary length
and wingcord length (5th primary length =—16.751+
0.107 xwingcord+0.0008 xwingcord2, R2 =0.98, Fig.
2B) we were able to estimate the length of the exposed
portion of the fifth primary in nestlings born in 2002
from their wingcord length. Thus, we could calculate the
proportion of nestlings having fault bars at different
portions of the fifth primary feather.
Age-related mortality
During our monitoring incursions into the colony, storks
returned to their nests just few minutes after our
To identify the ages at which nestlings are more sensitive inspection of nest content, and thus, our perturbation
to environmental factors we studied the age at which was supposed to be low. Moreover, we did a low number
they achieve homeothermy and the occurrence of fault of visits each year, and we inspected nest contents from
bars in feathers.
the ground with a mirror attached at the end of a long
To estimate the body mass at which nestlings achieve pole to minimise disturbance.
homeothermy (Mh) we applied the formula provided by
During seven years (1988, 1989, and from 1999 to
Visser (1998) for altricial bird species (Mh =1.58A0.765), 2003), the age of nestlings was recorded in six categories
Environmental sensitivity
(1: 1 — 10; 2: 11 — 20; 3: 21 — 33; 4: 34 — 45; 5: 46 — 59; 6: ]60
d-old) according to plumage development. We used this
data to assess nestling mortality at different age classes
according to the difference in the number of nestlings
present in successive inspections of the same nests.
We constructed a life table with the proportion of
nestlings of age categories from one to four present in
the first nest inspection that reached the fledgling age
(age class five). We rejected data from periods with final
ages above 60 d to ensure that the nestling could not be
fledged, or nest switched (Redondo et al. 1995), and thus
recorded erroneously as died. Moreover, we were able to
calculate the proportion of nestlings surviving from one
age class to the following, simply by dividing the
proportion alive in consecutive age classes.
Weather-related mortality
To study the effect of weather upon nestling mortality we
concentrated on nestlings B20 d-old, because mortality,
thermoregulation and fault bar information pointed to
the fragility of nestlings up to this age (see Results).
We calculated the Julian hatching date from the
wingcord length of the largest chick of 756 nests (see
Fig. 4 for sample sizes per year) according to the formula
provided by Negro et al. (2000):
Julian hatching date
= Julian measure date—(5:068
+0:117xwingcord length):
Since most chicks are born between 1 April and 15
May (see Results), we studied the effect of weather
variables on nestling mortality and nest fledging success
during this period. Daily meteorological data was
obtained from the nearest meteorological station (i.e.
‘‘Dehesa Nueva’’, Aznalcazar) placed 9 km from the
study colony (unfortunately, temperature was not registered in 1981 and 1982).
To estimate nestling mortality we selected those
observations concerning nests where nestlings were
recorded twice: when on their first age class, and again
when at least on their third age class. In this way, we were
able to extract unbiased estimates of the survival of
nestlings, since the critic period of mortality was over
passed (Table 1). Moreover, we rejected those data from
periods with final ages of >60 d for the same reasons
stated above.
Statistical analyses
Since the probability of dying of one nestling is
potentially related to the fate of their siblings, we
considered nestlings’ mortality as non-independent analysis units. Thus, to overcome the problems of pseudoreplication we analysed this data set with a generalized
lineal mixed model (GLMM), fitting the nest of rearing
as a random factor (Littell et al. 1996). We used a
binomial distribution of errors and a logit link function
to model the probability of surviving (1) or dying (0) of
each nestling. We tested the effect of total precipitation
between 1 April and 15 May and mean maximum
temperature during the same period, fitted to the models
as continuous variables.
Nesting success was calculated for ten years as the
proportion of nests with at least one nestling fledged
from the total of nests on which we recorded the
presence of a clutch. The correlation between breeding
success and weather variables was tested with a general
lineal model (GLM) with a binomial structure of errors
and a logit link function using the GENMOD procedure
of SAS. Rather than directly modelling the proportion of
successful nests per year we used as the response variable
the number of successful nests, and the total number of
nests inspected of the year was introduced as the
binomial denominator. In this way, the information of
the sample size (i.e. how many nests are inspected) of
each year was not loosed but used to give more
credibility to proportions calculated from larger sample
sizes (Crawley 1993). Total precipitation and mean
maximum temperature between 1 April and 15 May
were fitted to the model as continuous independent
variables.
Results
Weather
The general weather pattern during the study period
was an increase of temperatures from January to July
(Fig. 1A). However, at the annual level, this general
Table 1. Life table of white stork nestlings constructed from data on nestlings recorded at a certain age class and once again when
on the age class 5 (46 — 59 d-old). px =probability of survival up to the following age class. ‘‘% Total deaths’’ shows the proportion of
the whole deaths that are produced between each age class and the next one.
Age class
1
2
3
4
Age range (d)
Death
Alive
% Alive
px
% Total deaths
1 — 10
11 — 20
21 — 33
34 — 45
137
31
32
4
142
120
369
213
50.9
79.5
92.0
98.2
0.640
0.864
0.937
0.982
73.2
17.8
7.0
1.9
warming during the breeding season was stopped by
local decreases during cloudy and rainy (and frequently
windy) periods (Fig. 1B). Since precipitation during the
breeding season is highly variable among years (Fig. 1B),
this resulted in highly variable weather conditions
from year to year during the growth of nestlings
(Figs 1B and 5A).
Environmental sensitivity
According to the relationship found between nestling
age and weight (weight =—568.43+103.58 xAge —
0.61 xAge2; R2 =0.74, F2,1943 =2826.38, p B0.0001,
Fig. 2A), mean body mass before nest departure
(at 60 d of age) was 3450 g (Fig. 2A). Thus, according
to Visser (1998), nestlings of our white stork population
achieve homeothermy when they weight ca 804 g, that is,
at an age of 15 d (Fig. 2A).
The probability of nestlings developing a fault bar
decreased with feather elongation (2002: Spearman
r =—0.903, N =8, p =0.002; 2003: r =—0.830, N =8,
p =0.011), from being maximumum at distal feather
portions (ca 15 — 25 d of nestling age) up to being almost
absent from 100 mm onwards (at the age of ca 43 d or
more; Fig. 3).
Fig. 2. Relationship between age and weight (A), and between
wingchord length and 5th primary length (B) in white stork
nestlings.
Fig. 1. A) Average weather variables (rainfall, minimum and
maximum temperatures) from 1981 until 2003 in the study area.
B) Rainfall and maximum temperature between 1 April and 15
May for the years with available breeding data of white storks.
Fig. 3. Proportion of nestlings developing at least one fault bar
at different feather portions (0 =feather tip) of the 5th primary
feather.
Age-related mortality
Risk of mortality was clearly related to the age of
nestlings, survival expectancies rapidly increasing with
time. Highest mortalities occurred before achieving ten
days of age; nestlings from 11 to 20 d still suffered from
some mortality, but it was inappreciable at higher age
classes (Table 1). Overall, 91% of deaths occurred on
nestlings below 20 d of age, 73% concentrating on
nestlings up to 10 d-old.
Weather-related mortality
Most hatchings occurred from 1 April to 15 May
(Fig. 4). Mean maximum temperature during this period
showed a positive effect on nestling survival through
years (GLMM F1,886 =43.86, p B0.0001, Fig. 5B).
However, when we introduced both weather variables
into the model there was only a negative effect of rainfall
on nestling survival (GLMM F1,885 =49.02, p B0.0001,
Fig. 5B), the effect of mean maximum temperature being
not longer significant (GLMM F1,887 =2.64, p =0.1048).
The proportion of successful nests increased in
warmer years (GLM mean maximum temperature
x2 =16.11, DF =1, p B0.0001, Fig. 5C). However, multivariable GLM did not retained mean maximum temperature (GLM x2 =0.47, DF =1, p =0.4935), but only
a negative effect of total rainfall entered into the final
model (GLM rainfall x2 =69.77, DF =1, p B0.0001,
Fig. 5C).
Discussion
Our results show that sensitivity to environmental
stressors which could affect survival changes with age
Fig. 4. Hatching dates from 1998 until 2003 in the study colony
of white storks. Numbers inside boxes denote the number of
nests sampled. Boxes represent 25 — 75% and lines 10 — 90%
confidence limits, dots are rare hatching dates.
in nestling white storks. The body mass of white storks
just prior to fledge in our study population suggested
that nestlings do not achieve homeothermy until 15 d of
age. Accordingly, Tortosa and Castro (2003) found that
white stork nestlings acquire maximum homeothermy at
22 d of age, but that already around 15 d nestlings
achieve the inflection point on their gain of thermoregulation capacity. Moreover, fault bar occurrence
decreased through the nestling period, being more
frequent at times when nestlings were still not
fully homeotherms (i.e. until 25 d-old), and when
they were suffering of high mortality rates. Here,
we have found a high concordance of fault bar formation
with homeothermy acquisition and mortality risk
of nestlings. Interestingly, the same pattern of higher
fault bar occurrence at younger nestling ages have
been found in other studies (Machmer et al. 1992, Negro
et al. 1994). This suggests that fault bars could
be a useful tool to study when nestlings are physiologically more vulnerable and so exposed to a higher
mortality risk because of physiological reasons. Since
fault bars are easy to inspect from museum specimens,
this could be a good way of easily assessing the agedependent environmental sensitivity of newborn birds
through bird species and geographic areas, being of
special importance for studies on the effect of climate
change on birds.
Nestling mortality was highly age-dependent, decreasing through the nestling period, being especially high up
to 10 d of age and still important until 20 d, but
anecdotic afterwards. Both yearly nestling mortality and
nest failure was enhanced by rainy springs during the
period before nestlings acquiring homeothermy. However, contrary to our expectations, we found a positive
correlation of maximum temperatures with nestling
performance although it was not significant when
controlling for rainfall in multivariable models. The
negative effect of rain but not of high temperatures
could seem paradoxical according to the low latitude of
the study population. In fact, other bird studies
conducted in Spain and other warm climates have found
a negative effect of hot temperatures upon nestling
survival (Steenhof et al. 1997, Redpath et al. 2002),
suggesting that it could also apply to the white stork.
However, it is worthy noting that the high temperatures
recorded in June — August in the study area (e.g. 47.88C
in 23 July 1995, see also Fig. 1A) occur when most of
white stork nestlings are outside the age interval of high
mortality risk, and so, that it could not greatly affect
breeding success. Accordingly, in the species studied by
Steenhof et al. (1997) and Redpath et al. (2002) nestlings
spend their first weeks of live during the hot months of
May — July.
Another possibility is that severe weather indirectly
affects white stork mortality through food availability
(Ridpath and Brooker 1986, Pasinelli 2001) or the
Fig. 5. (A) Summary of weather
variables. Relationship between
annual survival of nestlings (B),
and nest fledging success (C)
with total rainfall and mean
maximum temperature between
1 April and 15 May.
foraging efficiency of parents (Dawson and Bortolotti
2000). We think that successive rainy days could
constrain the capacity of parent storks to forage and
adequately feed their offspring. Food availability, however, seem to be not an important factor since in our
study colony nestlings are mainly feed with red swamp
crayfish Procambarus clarkii (Negro et al. 2000), an
aquatic invertebrate which activity should be not greatly
affected by rain. Moreover, density-dependence could be
operating through intraspecific aggressions among
storks. In the wood stork Mycteria americana , reproductive success have been shown to be affected by
intraspecific aggression after nest failure of some pairs
of the colony due to bad weather (Coulter and Bryan
1995). We have observed (although not measured)
frequent takeovers in our study colony. Thus, we think
that these factors could be amplifying the detrimental
effect of weather inclemency upon nestling mortality in
white storks.
Previous researches conducted in central Europe
(Zink 1966) and Spain (Lá zaro et al. 1986) were early
aware of the negative impact of rainy springs
on the breeding success of white storks. More recent
papers have addressed this issue with more elaborated
analyses. Carrascal et al. (1993) found that in a
colony in central Spain the number of fledglings per
pair was lower in years with >10 d of rain during
May, but they did not found any correlation with mean
and minimum temperatures in May (although the
effect of maximum temperatures was not analysed). In
Switzerland, Moritzi et al. (2001) found a negative
effect of rain and a positive effect of temperature
occurring on May upon nestling survival. Interestingly
thus, white storks at different European latitudes
seem to be equally affected by rainy breeding seasons,
but not by high temperatures. In our study region (SW
Spain, Rodrı́guez and Bustamante 2003), and in the
Mediterranean basin in general (Anon. 2002), spring
precipitation is declining, and some models suggest a
continuation of this trend for the near future (Borén
et al. 2000). However, as has been previously stated
(Katz and Brown 1992, Wichmann et al. 2003) rain
variability rather than the trend of the mean could
be the important point here, and Easterling et al.
(2000) have reported that in the last decades extreme
precipitation events are increasing in the northern
hemisphere. Thus, according to the current global
climate scenario for the following decades (Anon.
2002), the negative effect of severe weather on the
survival of nestlings and the breeding success of white
storks is unclear, although it could be hypothesised that
will tend to increase in a global sense although with
punctual high breeding failures in particularly rainy
breeding seasons.
Acknowledgements — We want to thank Francisco Gabriel
Vilches for his effort collecting data between 1981 and 1989.
Many people helped on the field. We especially thank the
Equipo de Seguimiento de la Estació n Biológica de Doñ ana,
and Ma Carmen Quintero, Juan Manuel Terrero, Rosa
Rodrı́guez, Raquel Baos, Julia Blas, Guillermo Blanco,
Marcelo Bertellotti, Paco Rodrı́guez, and Marta Garcı́a.
Founds were provided by the Project B0S2002-00857 of
Ministerio de Ciencia y Tecnologı́a y Fondos Feder, Telefó nica
Mó viles S.A., and Junta de Andalucı́a.
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