Download Viability of an endangered population of ortolan buntings: The effect

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Viability of an endangered population of ortolan buntings:
The effect of a skewed operational sex ratio
Øyvind Steifetten*, Svein Dale
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
A R T I C L E I N F O
A B S T R A C T
Article history:
Population viability analyses (PVAs) are based on basic demographic parameters such as
Received 4 August 2005
breeding success, juvenile and adult survival. However, no PVAs have included the opera-
Received in revised form
tional sex ratio (OSR). The Norwegian population of ortolan buntings (Emberiza hortulana) is
1 March 2006
characterized by a strongly skewed OSR (almost half of all males are unpaired). We
Accepted 17 March 2006
assessed the effect of a skewed OSR on annual growth rate (k). Based on empirical data
Available online 6 May 2006
the deterministic growth rate was negative (k = 0.82), and mean time to extinction was
21.8 years. Model parameters had to be increased substantially compared to the empirical
Keywords:
mean values to achieve k = 1.0 (clutch size from 4.25 to 8.06, juvenile survival from 17.6% to
Population viability
33.5%, adult survival from 62.9% to 80.8%, and proportion of males breeding from 52.4% to
Demography
99.8%). Using low, mean and high empirical parameter estimates, we examined 36 scenar-
Operational sex ratio
ios for population development. Only five resulted in values exceeding k = 1.0, of which four
Unpaired males
required an OSR of 1:1. Correlations between inter-annual population change and demo-
Ortolan bunting
graphic parameters indicated that the proportion of males breeding matched the observed
population trend best. Because breeding success, juvenile and adult survival were within
the normal range seen among other bird species of similar body size, we suggest that
the skewed OSR is the major factor limiting population growth. Skewed OSRs may be particularly common in small and isolated bird populations, because of female-biased natal
dispersal, and should therefore be considered in all PVAs of endangered bird species.
Ó 2006 Elsevier Ltd. All rights reserved.
1.
Introduction
Population declines and range contractions are currently seen
in a number of bird species worldwide (e.g. Tucker and Heath,
1994; Fuller et al., 1995; BirdLife International, 2000; Chamberlain and Fuller, 2000; Cumming et al., 2001), and has become
an issue of great conservation concern. Human-caused
changes in the environment, such as habitat destruction
and conversion, are likely to be the ultimate causes for many
of these declines, and one of the most serious threats confronting the long-term survival of many species (Holsinger,
2000; Young and Clarke, 2000). However, even though the human-related factors causing population decline have been
identified in many cases (Newton, 2004), and measures to halt
or reverse these impacts have been taken, some populations
still decrease in numbers, suggesting that other factors could
be preventing population growth. One of these factors could
be intrinsic demographic problems. Thus, an understanding
of how demography affects population dynamics and population growth rates is essential when trying to recover small
and declining populations (Bro et al., 2000).
Basic demographic parameters such as breeding success,
juvenile and adult survival have all been suggested as the
proximate mechanisms that underlie the declines of many
bird species (Newton, 2004). For example, breeding success
has been suggested to be below levels needed to sustain
* Corresponding author: Tel.: +47 64965780; fax: +47 64948502.
E-mail address: [email protected] (Ø. Steifetten).
0006-3207/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biocon.2006.03.016
B I O L O G I C A L C O N S E RVAT I O N
viable populations among grassland bird species in North
America (Basore et al., 1986; Herkert et al., 2003), and also
among some farmland passerines in Europe (Bradbury et al.,
2000; Siriwardena et al., 2000), and juvenile and adult survival
have been implicated in the declines of several species found
in farmland habitat (Thomson et al., 1997; Peach et al., 1999;
Siriwardena et al., 1999; Bradbury et al., 2000; Bro et al.,
2000; Freeman and Crick, 2003; Robinson et al., 2004).
Although knowledge of breeding success and survival are
essential for understanding changes in population size (Paradis et al., 1998), other demographic parameters may be just as
important. One such parameter might be the operational sex
ratio (i.e., the ratio of males to females available for mating at
any one time; see Krebs and Davies, 1996). Dale (2001a) argued
that small and isolated bird populations are particularly
prone to skewed sex ratios, because of female-biased natal
dispersal, subsequently leading to a high proportion of unpaired males, and hence, reduced potential for population
growth. To our knowledge the operational sex ratio has never
been specifically assessed in population viability analyses, but
a male-biased sex ratio has been seen in several bird populations that are fragmented or isolated (reviewed by Dale, 2001a;
see also Walters et al., 1999; Bayne and Hobson, 2001; Zanette,
2001; Fraser and Stutchbury, 2004), indicating that the operational sex ratio should be given more attention in studies of
declining populations.
The ortolan bunting (Emberiza hortulana), a long-distance
migrant passerine, is one of several farmland birds that have
suffered major population declines in much of Europe (Cramp
and Perrins, 1994; Kutzenberger, 1994). As a result, it is listed
as depleted (i.e., species with unfavourable conservation status; Tucker and Heath, 1994), but does not meet any European
or global IUCN red list criteria (BirdLife International, 2004).
Agricultural changes, such as amalgamation of fields and
the consequent loss of breeding habitat together with reductions in crop diversity, have been implicated as the primary
causes of the decline (Lang et al., 1990), but the use of mercury-treated seed grains might also have played a role (Ree,
1992). In addition, about 50,000 migrant ortolan buntings were
estimated to be trapped each year in south-west France as a
‘‘culinary delicacy’’ in the early 1990s (Stolt, 1996), and trapping of ortolan buntings is still going on today. In Norway
the ortolan bunting was a common farmland bird until
around 1950, but has since declined dramatically (Haftorn,
1971), and is now classified as endangered on the Norwegian
red list (Størkersen, 1999). Although the primary causes of decline have been halted to some extent the population is still
decreasing, and current estimates of total breeding population size are 159–166 males, but fewer breeding pairs. Moreover, preliminary data on reproductive success, juvenile and
adult survival suggest that the population should have an annual rate of increase of 0.2% (Dale, 2001b). This indicates that
other factors than classical demographic parameters may be
involved in the observed decline, and the large proportion of
unpaired males may play a role (Dale, 2001b).
In the present six-year study we determined the current
demographic basis for the decline of the ortolan bunting.
Demographic parameters such as breeding success, juvenile
and adult survival, in addition to the operational sex ratio,
were incorporated into a population model to assess their ef-
1 3 2 ( 2 0 0 6 ) 8 8 –9 7
89
fect on annual population growth. In addition, we performed
a population viability analysis using mean empirical values in
order to assess the viability and long-term persistence of the
focal population.
2.
Methods
2.1.
Study area and population
The remaining population of the ortolan bunting in Norway is
restricted to an area of nearly 500 km2 in central Hedmark
County (60°29 0 –60°53 0 N, 11°40 0 –12°18 0 E), although a minor
sub-population exists approximately 60 km further south in
Akershus County. The birds occur in about 40 well-defined
habitat patches (raised peat bogs, forest clear-cuts on poor
sand, land being cleared for cultivation, and one forest burn;
Dale, 2001b), but always close to arable land, which is used for
foraging (Dale, 2000; Dale and Olsen, 2002). Dispersal among
habitat patches is frequently observed (Dale et al., 2005), but
dispersal between neighbouring populations is probably limited, as the nearest population is located approximately
250 km further east, in Sweden. The Norwegian population
lies at the edge of the species’ range.
2.2.
Data collection
The demography of the ortolan bunting was monitored annually during the breeding season (May–June) from 1996 to 2004.
The first three years of observations were mainly from a single
locality, whereas the last six years included the entire known
population in Norway. To avoid biases in demographic parameters between the two periods, only data from the latter was
used for analyses unless otherwise stated. Data on adult survival was obtained by capturing males in mist nets with the
aid of playback of song. For individual identification each male
was given a unique combination of three colour rings and one
numbered metal ring. In any year at least 55% of all adult males
had colour rings, but for most years the proportion of colourringed males were between 70% and 84% (range 103–133
males). Resightings were confirmed through regular visits
(1–3 days interval) to all habitat patches which have been used
by ortolan buntings. In addition, all other potentially suitable
habitat patches within the study area were visited frequently.
Because females behave cryptically throughout most of the
breeding season and do not aggressively respond to playback
of song, the capture and subsequent resightings of females is
difficult. Thus, little data on female survival is available, but
is assumed to be similar to male survival. This assumption
was based on the study by Dobson (1987) who showed that
there were no significant differences between male and female
survival in the majority of the fifteen species he examined, and
a preliminary analysis of our own data showed that male and
female survival rates did not differ (95% confidence interval:
males 0.593–0.671, n = 581 male-years; females 0.378–0.625,
n = 63 female-years). Resighting probability of returning males
was very high. Data on movements between years, showed
that for males recorded in at least two years, the time series
were continuous without ‘‘holes’’ (e.g., not recorded in year y,
but recorded in years y 1 and y + 1), in 216 out of 217 cases.
Hence, the use of mark-recapture modelling is not needed.
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The mating status of each male in the population was classified into one of the four following categories: (1) males with
a female (i.e. nest found, feeding behaviour, prolonged bouts
of warning calls, or seen together with a female most of the
breeding season), (2) males most likely with a female (i.e. reduced singing activity late in the breeding season, and/or
warning calls, but the female or any sign of nesting activity
was not observed directly), (3) males most likely without a female (i.e. seen with a female early in the breeding season, but
no signs of being mated during the egg-laying, incubation or
nestling period), and (4) males without a female (i.e. never
observed with a female, and sang frequently throughout the
breeding season). Although categories 2 and 3 are connected
with uncertainties regarding male status, most males
(90.2%) were assigned to either category 1 or 4.
During the nestling period as many nests as possible were
located and both clutch size and brood size were recorded.
Each nest was visited on a regular basis until nestlings had
left the nest. Nestlings were ringed with one colour ring and
one numbered metal ring, and returning male nestlings were
recaptured and given two additional colour rings. In total 621
nestlings were ringed (includes 11 fledged nestlings), but nestlings from 1996 to 1998 and 2004 (n = 251) were excluded in
survival analyses. Thus, juvenile survival was based on 370
nestlings, of which 185 were assumed to be males.
2.3.
Model parameters and underlying assumptions
In the present study, we explicitly wanted to examine the annual growth rate (k) in order to use it as a measure of population viability. For this purpose we used the computer program
VORTEX (Lacy et al., 2003), which is an individual-based simulation model for population viability analyses (PVA). Population growth under the influence of stochastic fluctuations
was calculated by random simulations repeated 1000 times,
whereas the deterministic growth rate was determined from
life table analysis. Although most data needed for analyses
were empirically available, some assumptions had to be made
for a few input parameters. First, since no data on maximum
breeding age, or life expectancy, exists for the ortolan bunting,
the value was arbitrarily set at 20 years to ensure that all males
and females in the population would be modeled as reproductively active until death. Although studies have shown that
reproductive success decreases with age (e.g. Robertson and
Rendell, 2001; Laaksonen et al., 2002), we have no reason to believe that old males cannot mate and reproduce (see Holmes
and Austad, 1995). Indeed, the oldest male in the focal population that has been observed breeding successfully was nine
years old. Second, we assumed a 1:1 sex ratio at birth. For
the PVA we also assumed that no catastrophes would occur
during the 100 years of simulation, and that there was no
inbreeding depression. Although two examples of close kin
mating (brother and sister, father and daughter) have been observed, nestlings fledged successfully in both cases.
Estimates of total population size were based on the number of marked and unmarked males in each year. Due to the
possibility of counting unmarked males twice because of
extensive within-breeding season and between-patch dispersal by adult males (Dale et al., 2005), we operated with a
low and high estimate in order to avoid the uncertainty of
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relying on a single value. Thus, males that were observed in
the same territory for 15 days or more were considered likely
to be the same individual throughout the breeding season,
and were included in the low estimate. On the other hand,
males that were observed in the same territory for 5–15 days
could potentially have moved to a different locality after the
last observation, increasing the likelihood of being counted
as a new individual. These males were included in the high
estimate. Single observations of unmarked males, or males
observed less than five days within the same territory were
considered to have moved to other territories and excluded
from the estimate altogether. However, unmarked males
showing feeding behaviour were included in the low estimate
regardless of the time observed.
As initial population sizes for the PVA we used the low,
mean, and high estimates of total population size for 2004.
There was a deficit of females in the population (see Section
3), which we assumed to be due to female-biased dispersal.
In VORTEX one does not have the option to model male and
female population size separately. Therefore, the deficit due
to dispersal was modeled as the proportion of females that
did not breed in a given year, and this approach will lead to
exactly the same results as if one could model female population size separately. Thus, the number of females incorporated into the population size estimates were equivalent to
the number of adult males, but those that were assumed to
have dispersed were modeled as non-breeders. In calculating
the mean proportion of males breeding (equivalent to the
operational sex ratio), we first assumed that all pairs that
were observed in a given year would also breed. Because we
used low and high estimates for both population size and
the number of breeding pairs, this resulted in four different
estimates of the proportion of males breeding in a given year.
The mean value of each year were then used to calculate the
overall mean value used for analyses.
As a measure of breeding success we used clutch sizes recorded during 1996–2004. Clutch size was used because we
also wanted to incorporate mortality that occurs at the incubation stage into juvenile survival estimates. In total 190
clutches were included in analyses. Note that although
Cramp and Perrins (1994) cited one study which reported a
clutch size of seven (1.1% of all clutches), the largest clutch
size observed in the focal population was five. To be conservative, we therefore set the maximum clutch size at six, which
is also the highest value normally reported for the ortolan
bunting (Cramp and Perrins, 1994). To define the proportion
of individuals within various cohorts, we used the stable
age distribution calculated by VORTEX for both males and females (see Miller and Lacy, 2003 for a definition of and methods for calculating this distribution). Carrying capacity was
set at 2000 individuals in order to avoid habitat availability
as a limiting factor. Environmental variation in carrying
capacity was assumed to be unchanged during the simulated
time period. Although some habitats are lost to new cultivation or other land uses, new breeding habitats are also created
through clear-cutting of forest, and the attractivity of some
peat bogs may have increased through cultivation of new
areas close to bogs. Thus, at present, habitat availability does
not seem to be limiting (Dale, 2001b). Summary of the input
parameters used for analyses are shown in Table 1.
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Table 1 – Summary of base parameter values for VORTEX population viability analysis
Mating system: monogamous
Age at first reproduction: 1 year for both sexes
Age at last reproduction: 20 years for both sexesa
Maximum clutch size: 6
Sex ratio at birth: 1:1a
Reproductive rates
Percent breeding: 52.4 (range 45–63; SD = 4.5)b
Exact distribution of clutch size within the population
1 = 0%; 2 = 2%; 3 = 14%; 4 = 43%; 5 = 41%; 6 = 0%
Mortality rates (%)c
Juvenile (0–1 year) for both sexes: 82.4 (range 73–89; SD = 6.3)
Unhatched eggs: 4.7 (range 3–9; SD = 2.4)
Egg predation: 0.5 (range 0–2; SD = 1.1)
Nestling mortality: 4.0 (range 0–15; SD = 6.1)d
Nestling predation: 5.4 (range 0–9; SD = 4.0)
Post-fledging mortality: 67.8 (range 64–69; SD = 2.2)
Adult (>1 year) for both sexes: 37.1 (range 28–47; SD = 7.1)
Males in breeding pool: 100%
Initial population size (set to reflect a stable age distribution): 325 (range 318–332)
Carrying capacity: 2000a
a
b
c
d
Parameter values based on assumptions (see text for further details).
Equivalent to the proportion of males breeding and the expected proportion of females breeding.
Values for mortality rates for both sexes are based on empirical data on male mortality.
Mortality due to starvation or severe weather conditions.
2.4.
Sensitivity analysis
In addition to modeling population persistence using the
empirical mean demographic rates, we also undertook sensitivity analysis in which we increased or decreased several
variables one by one while all other variables were held constant. For breeding success we modeled the population with
clutch sizes in steps of one from 1 to 6. Juvenile and adult survival were stepwise changed by three percentage points from
the empirical mean value, with extremes of 6% below and
15% above, whereas the proportion of males breeding was decreased to 30% and increased up to 100%. We also calculated
the specific value of each parameter that was needed to
achieve k = 1.0.
To examine the possible range of growth rates intrinsic to
the population, the low, mean and high empirical estimates
of juvenile and adult survival, and the proportion of males
breeding, were modeled for all different combinations of each
parameter. In addition, we included an operational sex ratio
of 1:1 because this is expected in most natural populations,
making the resulting values comparable to other studies.
However, low and high estimates of clutch size were omitted
from analyses because preliminary results showed that this
parameter had least effect on population growth, and also because annual fluctuations in clutch size was low (4.25 ± 0.28;
range 3.75–4.83). We therefore used the mean clutch size as
a standard variable in all analyses. A total of thirty-six different combinations were analysed.
Finally, we tested whether the yearly mean empirical values of each parameter could explain the observed change in
population size from one year to the next using correlation
analysis. We assumed that the magnitude of the correlation
coefficients of the four demographic parameters, would indicate their relative importance in explaining the observed population decline.
3.
Results
Based on the empirical data in Table 1 the deterministic calculations performed by VORTEX showed that population
growth rate was negative (k = 0.82) even in the absence of
any random fluctuations. When stochastic events were included in the model the corresponding growth rate was
0.81. This resulted in a probability of extinction within 100
years of 1.0, and mean time to extinction was 21.8 years
(range: 21.6–22.1 years).
The effect of increasing breeding success to a mean clutch
size of six was only of minor importance to population
growth (k = 0.91; Fig. 1a). For breeding success to achieve
k = 1.0 the mean clutch size had to be raised to 8.06, an almost
twofold increase from the empirical mean value of 4.25.
Increasing juvenile survival by 15%, on the other hand, had
a stronger positive effect on population growth (k = 0.99;
Fig. 1b). To achieve k = 1.0 the empirical mean value of 17.6%
had to be increased to 33.5%. For adult survival a 15% increase
resulted in a slightly lower growth rate than juvenile survival
(k = 0.97; Fig. 1c). The empirical mean value of 62.9% had to be
increased to 80.8% to reach k = 1.0. The only parameter that
resulted in a positive growth rate using the maximum preset
values was the proportion of males breeding. By increasing
the proportion to 100% the growth rate was slightly above
1.0 (k = 1.01; Fig. 1d). To achieve k = 1.0 the corresponding value was 99.8%. However, this is still an almost twofold increase from the empirical mean value of 52.4%.
The current range of possible growth rates based on low,
mean and high empirical values of survival and the proportion of males breeding, in addition to the value reflecting an
operational sex ratio of 1:1, are shown in Fig. 2. When we used
the low estimate of juvenile survival as a constant variable,
none of the different scenarios were able to bring k above
1.0 (range: k = 0.60–0.89; Fig. 2a). However, when the mean
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1.2
(a)
Population growth rate (λ)
Population growth rate (λ)
1.2
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1.1
1.0
0.9
0.8
0.7
0.6
(c)
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.5
1
2
3
4
5
55
6
60
65
1.2
1.2
(b)
1.1
75
80
(d)
1.1
Population growth rate ( λ)
Population growth rate (λ)
70
Adult survival (%)
Mean clutch size
1.0
0.9
0.8
0.7
0.6
1.0
0.9
0.8
0.7
0.6
0.5
0.5
10
15
20
25
30
35
30
40
Juvenile survival (%)
50
60
70
80
90
100
Breeding males (%)
Fig. 1 – Estimated population growth rates for different values of: (a) mean clutch size, (b) juvenile survival, (c) adult survival,
and (d) the proportion of males breeding. Open circles show the deterministic growth rate, and filled circles show the
stochastic growth rate with ±SD resulting from the simulations. The arrows indicate growth rates based on empirical mean
values.
value of juvenile survival was used as a constant variable we
found one scenario in which k was >1.0 (range: k = 0.70–1.10;
Fig. 2b). For the high estimate of juvenile survival the corresponding number of scenarios that exceeded k = 1.0 was four,
in which three were associated with scenarios that included
an operational sex ratio of 1:1, and only one that was associated with values based on empirical observations (range:
k = 0.78–1.26; Fig. 2c).
There were no relationships between inter-annual population change and clutch size (Spearman rank correlation:
rs = 0.20, n = 6, p = 0.65), juvenile survival (rs = 0.37, n = 6,
p = 0.41), and adult survival (rs = 0.37, n = 6, p = 0.41). However,
the proportion of males breeding contributed to explaining
the observed population trend, although not significantly so
due to small sample sizes (rs = 0.60, n = 6, p = 0.18). This indicates that the observed changes in population size from one
year to the next (mostly decline) is more likely an effect of a
skewed operational sex ratio, than by any of the other three
demographic parameters.
4.
Discussion
The ortolan bunting, in common with a number of other passerine species that occur on farmland, has experienced large
population declines. Analyses of long-term field data from
Norway showed that the deterministic growth rate was nega-
tive, and that the population is likely to go extinct within a
short period of time. One important reason to make deterministic projections is to obtain a general idea of whether
or not the rates of reproduction and survival are minimally
adequate to allow for population growth (Miller and Lacy,
2003). The present results suggest that the current causes of
the decline are more likely an effect of limiting demographic
rates, than by stochastic fluctuations alone. Among the
demographic parameters investigated, the operational sex ratio had the largest impact on population growth, suggesting
that the male-biased sex ratio observed in the focal population is the major force driving the decline. We stress the
importance of including operational sex ratios in population
studies of declining bird species, in particular those that have
small and isolated populations (Dale, 2001a).
4.1.
Breeding success
4.1.1.
Clutch size
The mean clutch size of 4.25 is close to the maximum number of five observed in the focal population, and also close to
mean clutch sizes of the ortolan bunting reported elsewhere
in Europe (see Cramp and Perrins, 1994). In addition, the observed distribution of clutch sizes matches to some degree
those found in other studies (see Cramp and Perrins, 1994).
Compared to other closely related species with a similar
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93
Fig. 2 – Estimated population growth rates expressed as a function of adult survival and the proportion of males breeding for
three estimates of juvenile survival: (a) low estimate (LE), (b) mean estimate (ME), and (c) high estimate (HE). The same three
estimates are used for both adult survival and the proportion of males breeding, but for the latter the maximum value (MAX)
is also given. All estimates are based on empirical observations.
population trend, i.e. reed bunting (E. schoeniclus), yellowhammer (E. citrinella), and corn bunting (E. calandra), the
mean clutch size in our study was similar or even higher
(Yom-Tov, 1992; Bradbury et al., 2000; Siriwardena et al.,
2000), indicating that clutch size is within the normal range
seen among other bunting species. Also, an increase of the
mean clutch size to the maximum number of six failed to
move k above 1.0, further supporting the view that female
fecundity is already high and unlikely to be the primary
cause of the decline. Furthermore, to achieve k = 1.0 the
mean clutch size had to be raised to 8.06, a value that has
never been observed in the ortolan bunting. Correlation
analysis also showed that there was no relationship between
inter-annual population change and clutch size, again suggesting that fecundity does not contribute to the decline.
4.1.2.
Nest mortality
Mortality that occurs at the incubation and nestling stages
may be a significant factor determining breeding success and
the subsequent number of new recruits into the population
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(Ricklefs, 1969). High mortality rates have been found in several studies of both grassland and forest-breeding birds in
North America (Anders et al., 1997; King et al., 1996; Porneluzi
and Faaborg, 1999; Pietz and Granfors, 2000; Herkert et al.,
2003), and losses up to 80–90% is typical of many neotropical
migrants occupying fragmented, urban, or agricultural landscapes (Robinson and Wilcove, 1994; Brawn and Robinson,
1996). Low breeding success until fledging has also been suggested to have driven the decline in the linnet (Carduelis cannabina) in Britain (Siriwardena et al., 2000), and of being too low to
maintain a stable population of the yellowhammer on lowland
farmland (Bradbury et al., 2000). Our data, however, show that
only a small proportion of eggs and nestlings are lost to either
predation, starvation or severe weather conditions (mainly
heavy rainfall) (see Table 1). Although a sampling bias (i.e. local
differences in predation or weather conditions) could have occurred due to small sample sizes during the first five years of
study (13–37% of all nests were found), in 2004 we found between 68–73% of all nests, and the different mortality rates
were comparable with those in previous years. We thus argue
that the mortality rates shown in Table 1 reflects the true range
of the focal population, and that they are normal or even below
values usually seen in ground-nesting species (see Ricklefs,
1969). Because mortality is already low the opportunity for
any substantial increase in survival is absent, and an increase
in survival will therefore have only a marginal effect on population growth rate.
4.2.
Post-fledging mortality
Our mean estimate of post-fledging mortality (67.8%), here
defined as mortality that occurs after fledging until the return
as first year breeder, is substantially lower compared to many
other species of similar body size (see Weatherhead and Forbes, 1994). Also, when we compared total juvenile mortality
(i.e. mortality imposed at the incubation stage and onwards
until the return as first year breeder) with the same dataset,
our estimate was still lower for all but two cases (n = 65). It
should be noted, however, that natal dispersal can be extensive, thus some locally studied populations may have overestimated mortality rates (Weatherhead and Forbes, 1994).
Although Siriwardena et al. (1998) found higher first-year survival rates in a study of twenty-eight passerines, these estimates were based on survival rates of young birds ringed
after independence, and excludes nestling mortality and mortality that occurs immediately after fledging. Thus, their values cannot be compared directly with ours. It seems that
our estimates of juvenile mortality are well within, and even
below, the range normally seen in small passerines. As such,
an increase of total juvenile survival from 17.6% to 33.5%,
which is required to achieve k = 1.0, is probably unrealistic
and not normal for small long-distance migrant passerines.
A study by DiQuinzio et al. (2001) on the saltmarsh sharptailed sparrow (Ammodramus caudacutus) found annual return
rates for juveniles ranging from 0% to 44%, hence showing
that survival may be high in some years, but they argue that
the mean values (11.6–29.7% depending upon year and site)
fall at the upper end of documented return rates for migrants
and well within typical return rates for residents. In support
of the view that juvenile survival in our population is reason-
1 3 2 ( 2 0 0 6 ) 8 8 –9 7
ably high, and therefore most likely not the major limiting
demographic parameter, we found no relationship between
inter-annual population change in abundance and juvenile
return rates, indicating that population trend is unaffected
by changes in juvenile survival rates. Moreover, Weatherhead
and Forbes (1994) found that most migratory species that
exhibited high juvenile returns were from isolated populations, although extrapolating these results to the study population should be taken with great caution. Nonetheless, it is
possible that the juvenile survival estimate found in this
study could be somewhat higher, partly because a substantial
number of ortolan buntings are trapped each year in France
during migration (Stolt, 1996). Although both adults and juveniles are susceptible to trapping, juveniles might be more naive than adults, thus experiencing higher mortality.
4.3.
Adult survival
Compared to other passerines of similar body size (both sedentary and migratory species) the mean adult survival rate
(62.9%) was high (see Botkin and Miller, 1974; Sæther, 1989;
Dobson, 1990; Johnston et al., 1997; Siriwardena et al., 1998;
Siriwardena et al., 1999; Peach et al., 2001 for reviews). However, the compared data sets are mostly based on markrecapture studies faced with the problems of distinguishing
between permanent emigration and mortality, and may thus
underestimate survival rates. Although one can argue that we
are faced with the same problems, data on movements between years showed that we had an almost 100% resighting
probability (Dale et al., 2005). Nonetheless, this suggests that
adult survival is unlikely to be the limiting factor for population growth. This is further supported by the finding that to
achieve a positive growth rate in the population, mean adult
survival had to be raised above 80.8%. To our knowledge such
an estimate has never been recorded for a long-distance migrant passerine of small body size. Moreover, tropical passerines, which are thought to have a higher survival rate than
species in northern temperate regions, rarely have survival
rates above 80% (Johnston et al., 1997; Peach et al., 2001).
The fact that there was no relationship between inter-annual
population change and adult survival again show that this
parameter is unlikely to have driven the recent decline.
4.4.
Operational sex ratio
One striking feature of the focal population was the skewed
operational sex ratio with almost twice as many males as females. Several explanations may account for this apparent
discrepancy. First, there may be a skewed sex ratio at birth.
Numerous studies have shown that individual birds make
adjustments of primary sex ratios that appear to be adaptive
(e.g. Komdeur et al., 1997; Bradbury and Blakey, 1998; Nager
et al., 1999; Whittingham and Dunn, 2000), but this does not
affect the population-level sex ratio. Furthermore, in the
study population a male-biased sex ratio would be maladaptive, because there already is an excess of reproductive males.
We therefore believe that the assumption of a 1:1 sex ratio at
birth is reasonable, and that the skewed operational sex ratio
is due to some other cause. Second, adult mortality could differ between sexes due to differences in ecology (Siriwardena
B I O L O G I C A L C O N S E RVAT I O N
et al., 1998). Although slightly higher mortality rates have
been found for females than males in some species, very
few species-specific examples have been statistically significant (Dobson, 1987; Siriwardena et al., 1998). Moreover, even
if there is a sex-biased mortality in the focal population, it
is unlikely to produce a sex ratio with almost twice as many
males as females, which is the current situation. Furthermore, sex-biased mortality is expected to be more common
in sexually size-dimorphic species than in sexually sizemonomorphic species (Ellegren et al., 1996) such as the ortolan bunting. A third possibility, proposed by Dale (2001a), is
that a male-biased sex ratio may be the consequence of female-biased natal dispersal. In small and isolated populations some females may disperse away from the local
population through natal dispersal, subsequently being lost
from the breeding pool. As a result, this will lead to a skewed
operational sex ratio with a high proportion of unpaired
males. A similar trend has been observed among other species with a high degree of isolation (see Dale, 2001a). In the
ortolan bunting, we found that only 52% of all males paired
with a female, and such a value is likely to be detrimental
for total reproductive output and the subsequent recruitment
of new individuals into the population. Our data also showed
that the proportion of males breeding had a large influence on
population growth rate. Increasing the number of males that
breed to 100%, which is usually assumed to be the case in
most natural populations (Dale, 2001a), resulted in a stable
growth rate (k = 1.01). Also, sensitivity analyses showed that
this maximum value was needed to achieve k > 1.0 in four
of the five scenarios that gave a positive growth rate. Finally,
we found a positive trend in the relationship between interannual population change and the proportion of males breeding. These results suggest that the skewed operational sex
ratio, through the proportion of males breeding, could be an
important determinant of population growth rate in the focal
population, and hence, population viability.
5.
Conclusions
All PVA models are more or less simplified versions of real life,
and thus, susceptible to errors at a number of stages. To obtain meaningful data from any PVA-programme, one has to
have extensive knowledge of the focal species’ biology, in
addition to a lot of data, so that it represents the wild population as accurately as possible. In this study we think that both
criteria are fulfilled. Although some assumptions have been
made in the model, most parameters (at least the critical
ones) were based on extensive field data. We therefore believe
that the simulations reflect the demographic situation of the
focal population well. At least for two of the four parameters
investigated (i.e. breeding success and adult survival) it seems
reasonable to conclude that they are not the cause of the current decline, because the estimated values falls within, or
even above, the range seen among other species of similar
body size. We suggest that the proportion of males breeding
is the principal factor limiting population growth. Even so,
increasing the proportion of males breeding to 100% only resulted in k being slightly above 1.0, thus making the population potentially unstable in the long run when stochastic
events are taken into consideration. In fact, the correspond-
1 3 2 ( 2 0 0 6 ) 8 8 –9 7
95
ing stochastic growth rate was 0.95. Therefore, it is likely that
also juvenile survival may have an effect on the population
development in the ortolan bunting. Sensitivity analyses
showed that when the proportion of males breeding was set
at 100%, four out of five scenarios required juvenile survival
to be set at the high estimate (27%) to achieve k > 1.0. Thus,
for long-term stable population dynamics it seems likely that
an increase in both parameters are needed. Nonetheless, in
this study we have shown that a skewed operational sex ratio
has great impact on population viability. To our knowledge no
studies have used this as a model parameter in the evaluation
of population declines. One study by Cooper and Walters
(2002) suggested that patch isolation was responsible for the
high proportion of unpaired males in fragmented habitat,
and related this to a lack of female immigration, which parallels the situation for the small and isolated Norwegian population of ortolan buntings, where female emigration is
probably much higher than female immigration. Also, Brook
et al. (2000) argued that sex ratio was an important determinant for correctly predicting extinction risks between different PVA packages. They pointed out that matrix-based
packages do not consider the possibility of a temporarily
skewed sex ratio, and may thus underestimate extinction
risks compared to individual-based packages (e.g. VORTEX)
which do. They stress the importance of incorporating demographic uncertainty in the sex ratio in PVAs. Nevertheless, it
seems that the operational sex ratio has been overlooked in
many population studies in birds, and that skewed operational sex ratios may be particulary common in small and isolated bird populations because of female-biased natal
dispersal. Future studies of declining species should therefore
incorporate data on operational sex ratios, and analyse the
relative contribution of breeding success, survival and operational sex ratios on population viability.
6.
Implications for conservation
The operational sex ratio is an important quality of any population, and directly affects the reproductive output and the
number of new recruits into the population. As such, it is a vital demographic parameter in the evaluation of population
declines. Although skewed operational sex ratios may arise
due to different causes, we argue that female-biased natal
dispersal is likely to occur in small and isolated bird populations (see also Dale, 2001). This means that because small
populations are usually also small in range and size, any female that disperse through natal dispersal has the potential
of being lost from the breeding pool. Furthermore, because
of isolation there will be limited immigration of females (or
none depending upon the degree of isolation). Thus, the number of females that emigrates will exceed the number that
immigrates, leaving the population with an operational sex
ratio that is male-biased. As a result, the reproductive output
of the population will decrease (equivalently to the operational sex ratio), which again will reduce population size. This
will subsequently make the population more vulnerable to
stochastic events, which in turn may lead to extinction.
Conservation actions designed to restore the natural operational sex ratio can be classified into two categories: (1) measures to prevent female emigration, and (2) measures to
96
B I O L O G I C A L C O N S E RVAT I O N
increase female immigration. Direct actions to prevent
females from emigrating seems difficult, especially in highly
mobile animals such as birds. However, creating suitable habitat patches around the population may reduce the number of
females that are lost from the core areas, because these
patches may act as ‘‘conspecific male traps’’ that attracts
females to settle. In the long term, natural selection might increase female philopatry and reduce dispersal distances, but
we have no information on the time span involved, or, indeed,
if there is enough heritable variation in dispersal behaviour for
selection to act on. In highly isolated populations (which is the
case for the Norwegian population) natural immigration of
females is not likely to occur. The only conservation measure
available then is the introduction of females from other
populations. However, such actions are connected with uncertainties as to whether females will stay in the site of introduction, and whether the females will return in future years.
Moreover, this is still just a temporary solution, and will have
no long-term effect on the operational sex ratio. In summary,
the scope for management actions to offset the negative effects of female emigration from small populations seems to
be disappointingly small. Thus, management actions need to
be implemented before the population size decreases below a
threshold value where this kind of Allee effect starts to operate.
Acknowledgements
We thank V. Bunes, P. Christiansen, J.P. Cygan, A.K. Darrud, T.
Granerud, H. Gregersen, B. Hessel, H. Johansen, A. Lambrechts, B. Lelaure, A. Lunde, N. Manceau, A.K. Narmo, B.F.G.
Olsen, T. Olstad, T. S. Osiejuk, K. Ratynska, T.I. Starholm, C.
Sunding and K. Sørensen for assistance in the field, and J.
Swenson for comments on the manuscript. The study was
financially supported by Selberg legacy/WWF Norway, Mr.
and Mrs. Sørlie’s Foundation, the Norwegian Directorate for
Nature Management and the Environmental Authorities of
Hedmark and Akershus Counties.
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