Download Environmental factors affecting the densities of owls in

Biologia 67/6: 1204—1210, 2012
Section Zoology
DOI: 10.2478/s11756-012-0114-x
Environmental factors affecting the densities of owls
in Polish farmland during 1980–2005
Michal Żmihorski1, Jerzy Romanowski2,3 & Przemyslaw Chylarecki1
Museum and Institute of Zoology, Polish Academy of Sciences, Wilcza 64, 00–679 Warsaw, Poland; e-mail:
[email protected]
2
Centre for Ecological Research, Polish Academy of Sciences, Dziekanów L. near Warsaw, 05–092 L
omianki, Poland
3
Faculty of Biology and Environmental Studies, UKSW, Wóycickiego 1/3, 01–938, Warsaw, Poland
1
Abstract: During the last decades, farmland habitats in central European countries have changed significantly, seriously
affecting populations of many farmland bird species. We compiled available published data on densities of three owl species,
Athene noctua, Asio otus and Strix aluco collected in the Polish farmland. All results of censuses based on the playback
method conducted between 1980–2005 were included in the analysis. The proportions of grassland, fields, built-up land and
forest at each studied plot were estimated and used as predictors in additive models. Proportions of main land use types,
extracted with the principal component analysis, explained much of the variation found in owl densities, although some
of the relationships were nonlinear. In general, owl densities were found to be affected positively by a high percentage of
grasslands and built-up land, and negatively by the amount of fields and forests. Little owl densities showed a significant
negative trend over the study period. It seems that high prey availability is an important factor accounting for the positive
relationship between grassland proportion and owl density. The significant decrease in grassland areas and increase in forest
coverage that were recently recorded in Poland may thus negatively affect populations of the three owl species studied here.
Key words: little owl; long-eared owl; tawny owl; grassland; habitat preferences
Introduction
Land use and the spatial structure of farmland habitats in Central and Eastern Europe have changed significantly during the last decades, which is partially associated with the enlargement of the European Union
and large-scale economic and political transformations
(Donald et al. 2006). In general, two distinct patterns
of farmland transformation can be observed: intensification on one hand and abandonment of agriculture on
the other (e.g., Verhulst et al. 2004). Both processes
have been shown to negatively affect many farmland
bird species (Newton 2004; Atkinson et al. 2005). The
intensification of agriculture is connected with mechanisation, herbicide and insecticide use, earlier ploughing
of stubble or an increase in the intensity and frequency
of mowing and grazing (Newton 2004). These factors
may lead to a decline in the abundance of invertebrates
and availability of seeds, important diet components of
many farmland bird species (Wilson et al. 1999; Atkinson et al. 2005; Fuller et al. 2005). The abandonment
of agriculture indirectly leads to the considerable transformation of vegetation cover as a consequence of secondary succession (Baur et al. 2006), resulting in a
decreased availability of invertebrate prey to avifauna
(Hoste-Danylow et al. 2010). Moreover, in many European countries, some parts of open habitats, especially
less productive ones, have been afforested within the
c 2012 Institute of Zoology, Slovak Academy of Sciences
framework of programs to increase forest cover. This
has had an additional negative impact on many farmland birds (Diaz et al. 1998; Marchesi & Sergio 2005).
The transformations of farmland habitat resulted in the
decline of many bird species inhabiting the open landscape in Central and Eastern Europe (Donald et al.
2006; Chylarecki & Jawińska 2007; L
awicki et al. 2011).
Unfortunately, knowledge about the population
dynamics of particular species is still very general, while
the ecological processes driving the dynamics of farmland birds in Central and Eastern Europe are relatively
poorly known (Tryjanowski et al. 2011). As a consequence, effective management of farmland biodiversity
with the help of, e.g., agri-environmental schemes is
difficult. Fundamental knowledge on the dynamics of
particular components of overall biodiversity in farmland is still lacking, especially in less developed countries (Tryjanowski et al. 2011). The aim of this study
was to analyse the relationship between land use and
the density of three Strigiformes owl species inhabiting
the Polish farmland. Apparently, knowledge concerning
population changes of these owls is relatively poor due
to the fact that common bird monitoring schemes are
not reliable in the case of nocturnal birds, as they are
difficult to detect. However, it appears that data on the
owls’ occurrence and densities are important because
these species seem to be reliable indicators of ecosystem value. Owls are important predators in farmland
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Table 1. Densities of three owl species in controlled plots in 1980–2005 in Polish farmland.
Territories * 10 km−2
Plot no.
1
2
3
4
5
6
7
8
9
10
11
12
13
Area [km2 ]
10.0–11.7
264.0
80.0
91.0
11.0
18.0
22.0
10.0
50.0
35.0
70.0
7.5
25.7
N of censuses [years]
Study period
13
1
1
1
4
5
2
4
1
1
1
3
1
1982–2005
2002
1984
2002
1980–1995
1982–1996
1980–1986
1984–2003
1989
1989
1989
1984–1986
2004
LO
LeO
TO
0–6.84
0.34
0.38
0.33
0–1.81
1.11–1.67
0.45
0–2.00
0.00
0.00
0.14
0.00
1.95
0–5.13
No data
0.63
0.22
0–2.72
0.56–1.67
0.91
0–4.00
0.60
0.00
0.00
0–4.00
No data
0.85–2.56
No data
1.00
1.21
0.45–1.81
0.56–1.67
0.91–1.14
0.00
1.40
1.14
1.00
0.00
No data
Explanations: LO – little owl Athene noctua, LeO – long-eared owl Asio otus, TO – tawny owl Strix aluco. Source of data (according
to plot no.): 1– Romanowski (1988), Dombrowski et al. (1991), Bacia (1997), Żmihorski et al. (2006), Kowalski, unpublished.; 2 –
Kasprzykowski & Golawski (2006); 3 – Fronczak & Dombrowski (1991); 4 – Dombrowski et al. (2004); 5 – Dombrowski et al. (1991);
Golawski & Dombrowski (2004); 6 – Dombrowski et al. (1991); Golawski & Dombrowski (2004); 7 – Dombrowski et al. (1991); 8 –
Dombrowski et al. (1991); Żmihorski & Osojca, unpublished.; 9–11 – Jermaczek et al. (1990); 12 – Dombrowski et al. (1991); 13 –
Żmihorski (2004).
habitats (Goszczyński 1977, 1981). They compete with
other predatory vertebrates and reduce prey populations (Korpimaki & Norrdahl 1989; J˛edrzejewski et al.
1994). Moreover, Sergio et al. (2006) proved that owls
are good indicators of the species richness of several
taxa of animals and plants, as well as of species diversity
in bird communities. Therefore, patterns of the spatiotemporal variability of the owls’ density in farmland can
be treated as a reliable indicator of changes in overall
biodiversity value. More specifically, we attempted to
check whether different species prefers different habitats
in agricultural landscape. The three species included
in the analysis have distinct ecologies and life histories
that make them likely to respond differently to land use
composition and long-term landscape changes. Moreover, we would like to test long-term trends of densities
of particular species in the context of overall biodiversity los in agrocenoses. We expect to show long-term
negative trends in densities of the little owl which is
highly dependent on extensive agriculture and to provide some new data on the habitat use of three focal
species.
Material and methods
The analysis was based on published data from census plots
situated in the lowland agricultural habitats (with forest
coverage <50%) of Poland (north of 51◦ N). Most of the
census plots were located in the Mazovia Lowland (EastCentral Poland), with a few in the Lubuska Lowland (Western Poland). Data from upland and mountain regions were
not included since their specific climatic and habitat conditions may influence the relationship between owl populations and their habitats. Three owl species were analysed:
the little owl Athene noctua (Scopoli, 1769), the long-eared
owl Asio otus (L., 1758), and the tawny owl Strix aluco L.,
1758. The barn owl Tyto alba Scopoli, 1758 occurred only
occasionally in the study plots and therefore was not taken
into account in the present analysis.
All 1980–2005 studies on owl densities in the Polish
farmland were reviewed (see Table 1 for references). Only
results of censuses based on the playback method were analysed, as this is the most efficient and reliable method of surveying for owls (Zuberogoitia & Campos 1998). In case of
majority of studies used in the analysis, the paper of Domaszewicz et al. (1984) is referred to as a methodological
protocol. The methods applied in all the studies use broadcasting of male voice of a given owl species during night
controls, with each broadcasting being followed by several
minutes of listening of responding birds, and all recorded
owls being marked on maps. The distribution of playback
points is designed in such a way that enables coverage of
the entire plot. At least two independent controls are conducted at each plot during breeding season. Altogether, 38
separate censuses, conducted at 13 different plots were analysed. Data on land use of the study plots were obtained from
the analysed papers. When the original data were not provided, it was computed with the help of Arc View 3.0 on
the basis of digitalised topographic maps. For each census
plot, the percentages of four main land use types were computed: Grassland – meadows and pastures, Field – arable
land and fallows, Forest – all afforested areas, and Built-up
– all built-up land, including single buildings.
As a first step we conducted linear regression in order
to find associations between densities of the three species
and original variables. However, percentages of the four
main land use types equalled 100% for each census plot
and were negatively inter-correlated across the study plots.
Therefore, we applied principal component analysis (PCA)
to reduce the original variables to two orthogonal (i.e. statistically independent) components, which were then used as
explanatory variables in further statistics. We used general
additive mixed models (GAMM) implemented in the R program (R 2012) with the help of the ‘mgcv’ package (Wood
2006). The effect of plot was used as the random categorical
effect, the year was fitted as the linear covariate, whereas the
two principal components describing habitat variables were
fitted with splines, as we expected a nonlinear relationship
between species density and habitat gradients. The method
of thin plate penalized regression splines enabled us to fit
nonlinear patterns of relationships between dependent and
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M. Żmihorski et al.
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Table 2. Correlation coefficients of the two components extracted
by the principal component analysis (PCA) with varimax rotation and original variables describing land use of the studied plots.
Correlations with r > 0.5 are marked in bold.
Original variable
Grasslands
Arable fields
Forests
Built-up areas
PC1
PC2
0.63***
–0.97***
0.45**
0.87***
0.53***
0.34*
–0.96***
0.03
correlation, rho = 0.48, P = 0.0102) whereas no correlation was recorded between density of the little owl
and that of the tawny owl (rho = 0.26, P = 0.208).
Densities of the long-eared owl and the tawny owl were
negatively correlated, bordering the conventional level
of statistical significance (rho = –0.36, P = 0.0783).
In the univariate approach, densities of the little
owl were positively correlated with grasslands (Linear
regression, t = 3.61, df = 36, P = 0.0009) and negatively with forests (t = 2.18, df = 36, P = 0.0363).
For the long-eared owl, only negative association with
forests was recorded (t = 2.38, df = 25, P = 0.0254).
Densities of the tawny owl were positively correlated
with percentage of grasslands and build-up areas (t =
2.80, df = 23, P = 0.0103 and t = 2.62, df = 23, P =
0.0153, respectively) and negatively with the percentage of fields (t = 4.07, df = 23, P = 0.0004). Moreover,
in case of this species positive correlation with forest
was recorded on the verge of statistical significance (t
= 1.83, df = 23, P = 0.0807).
Two components were extracted with the PCA on
habitat variables and used for further analysis (Table 2). The component 1 (PC1) represents a gradient
between built-up areas and grasslands on one side (positive values of the component) and arable fields on the
other (negative values). The component 2 (PC2) should
be interpreted as a gradient between forests (negative
values of the component) and grasslands (positive values).
Temporal trends and land use characteristics explained from 15% (long-eared owl) to 65% (little owl)
of variation in the density at the census plots. Generalized additive mixed models showed significant decline
of the little owl densities over time (Table 3). Densities of the two remaining species did not show any
Fig. 1. Distribution of densities of the three owl species in farmland landscape in Poland during 1980–2005 estimated with the
kernel density estimator. For abbreviations see Table 1.
independent variables. The addition of a penalty for smooth
function based on generalized cross validation (GCV) helps
to estimate an optimal degree of smoothness as a trade off
between model fit and model simplicity (Wood 2006).
Results
Densities of the studied species differed to a great extent
between plots and over time. All of the three species
studied did not occur in some of the plots and in some
years (Table 1).
Densities of the three species studied averaged 1–
1.5 territories per 10 square kilometres and the majority
of recorded densities did not exceed 2 territories/10 km2
(Fig. 1).
Densities of the little owl and the long-eared owl
were significantly positively correlated (Spearman rank
Table 3. Summary of the generalized additive mixed models (GAMM) explaining densities of the three owl species in the Polish
Lowland in 1980–2005.
Source
Linear fit
Intercept
Year
Spline
PC1
PC2
LO (R2adj = 0.65)
LeO (R2adj = 0.15)
TO (R2adj = 0.55)
All (R2adj = 0.49)
Estimate
P
Estimate
P
Estimate
P
Estimate
P
B = 15.9
B = –0.008
0.022
0.024
B = –5.7
B = 0.002
0.588
0.579
B = 5.8
B = –0.003
0.155
0.161
B = 3.62
B = –0.001
0.826
0.841
Edf = 3.47
Edf = 2.38
0.001
0.002
Edf = 1.00
Edf = 1.00
0.428
0.011
Edf = 1.00
Edf = 1.95
<0.001
0.026
Edf = 1.00
Edf = 1.00
<0.001
<0.001
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Fig. 2. The relationships between the density of the three owl species and the land use on the study plots, expressed by the two
components from PCA (see Table 2 for details).
trend. The modelling applied revealed significant linear and nonlinear relationships between density of owls
and two principal components representing variation in
land use (Table 3). The PC2 was the only predictor significantly explaining the density of the long-eared owl
and this relationship was linear. The maximum densities of the species were associated with a high proportion of grasslands and low proportion of forests at
census plots (Fig. 2). Density of the tawny owl showed
a positive linear dependence on the values of PC1 (i.e.
the species preferred built-up areas and grasslands and
avoided arable lands) (Fig. 2). For this species, habitat
characteristics described by the PC2 were also significant, but in a slightly nonlinear manner, suggesting
a preference for the intermediate values of this vari-
able. Densities of the little owl were strongly explained
by the two extracted components, but the relationships
were nonlinear (Table 3). This species reached highest
densities at moderate values of the PC1, with lowest
densities at moderate values of the PC2. The densities
of all three species pooled together were highly significantly positively dependent on both components (PC1
and PC2) and the linear fit was the best in both cases
(Fig. 2).
Discussion
In general, densities of the three owl species in the Polish farmland are low as compared to other data from
Europe. The densities of the little owl reported from
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Western and Southern Europe often exceed 10 territories per 10 km2 (see review in Génot & Van Nieuwehuyse 2002), while in Poland they are ca 10 times
lower. The maximal density of nearly seven territories
per 10 km2 was recorded in the suburbs of Warsaw
in 1986, nevertheless this species disappeared from the
area by 2003 (Żmihorski et al. 2006). This discrepancy
between Western and Southern Europe and Poland may
be associated with the more severe climate conditions in
Poland than in the west and south of Europe, and with
the close distance to the northern border of the species’
range. The tawny owl seems to be the most common
owl in Poland (Tomialojć & Stawarczyk 2003), yet its
densities in the Polish farmland are lower relative to
those observed in the farmland of UK and Italy (Galeotti 2001). However, this species reaches much higher
densities in forest habitats than in farmland (e.g., Galeotti 2001 and references therein). The densities of the
long-eared owl are similar to those reported from Europe (Cramp & Simmons 1980).
In general, the analysis of the effect of land use on
owl densities showed that they are positively affected by
the proportion of grasslands and built-up land and negatively affected by the percentage of forests and arable
fields. The positive correlation between grassland proportion and owl densities may be driven by the specific vegetation structure of this habitat type. It was
shown that the availability of prey for many diurnal
and nocturnal birds of prey is high in meadows and
pastures relative to fields and fallows (e.g., Aschwanden et al. 2005; Mirski 2009). This results from mowing
and grazing, i.e., activities reducing the height of vegetation, affecting the foraging of birds on epigeic prey
(such as invertebrates and rodents; Sheffield et al. 2001;
Hoste-Danylow et al. 2010). Reduced vegetation cover
enables them to achieve high hunting success, leading to
the documented preference of many raptors for grassland habitats (Butet & Leroux 2001; Tome & Valkama
2001; Poulin et al. 2005; Mirski 2009; Zub et al. 2010;
this study) despite that absolute abundances of prey
may decrease as a result of grazing and mowing (HosteDanylow et al. 2010; Fabriciusova et al. 2011). Selection
of grasslands was already confirmed in a study of habitat preference of the little owl (Żmihorski et al. 2009).
In this species earthworms and insects constitute important component of diet and this prey can be captured most effectively in areas with low vegetation –
most easily on grazing pastures and mown meadows.
Romanowski & Żmihorski (2008) showed that a high
proportion of grasslands corresponded to a high percentage of long-eared owl’s favourite prey, i.e., Arvicolidae, in the diet. The negative effect of the high proportion of fields in the census plots on owl densities
may be partially accounted for by the agricultural activities that reduce the density of the owl’s prey, e.g.,
voles and invertebrates (Wilson et al. 1999; Jacob &
Hampel 2003). Moreover, in our study, this land use
category includes fallow land, where vegetation is especially high and thick. In the case of our study plots,
this may be driven by the recent expansion of alien
M. Żmihorski et al.
weeds (mainly Solidago canadiensis) that may negatively affect biodiversity of farmland and habitat use
by birds (Skórka et al. 2010). Therefore, we expect that
the negative effect of arable fields expressed with PC1
on the density of the little owl, the tawny owl and of
the three owl species pooled is associated with lower
prey availability and hunting success in this habitat.
The negative effect of forest cover on the density of the
little owl and the long-eared owl is rather obvious and
should be related to their foraging habits (Cramp &
Simmons 1980; Génot & Van Nieuwehuyse 2002). Interestingly, the percentage of forest cover at the plots
had no effect on the tawny owl’s density. This is somewhat unexpected because this species commonly occurs
in forests (Galeotti 2001). The most probable explanation of this pattern is the low age of farmland woods
– the average age of small forest patches in the agricultural landscape is usually too low to ensure appropriate nesting and roosting conditions for the tawny
owl, which uses holes in old trees (present, e.g., in
parks and gardens) for breeding. The density of the
tawny owl was positively correlated with the percentage of built-up land and grasslands (PC1) at the farmland study plots. One cannot exclude that percentage of buildings is correlated with other habitat features that provide nest sites (e.g., old parks) for this
species.
The interpretation of the habitat preferences of
owls recorded in this study in the context of recent
changes to Polish farmland is not optimistic. In 1995–
2005, grassland coverage in Poland declined significantly by 10% (Linear regression; r = –0.79; n = 11; P
= 0.003), with a simultaneous increase in forest cover
by 4% (r = 0.99; n = 11; P < 0.001; data after Central Statistical Office in Poland). As shown, the density
of the three owl species is positively correlated with
grassland availability and negatively with forest coverage. Therefore, the observed changes of land use structure can be assessed as negative for the existence of
these birds of prey. An analysis of factors driving the
decline of the little owl seems to confirm this hypothesis (Żmihorski et al. 2006; Salek et al. 2008). If owls are
considered an indicator of high species richness of other
animals and plants (Sergio et al. 2006), the population
changes of these birds of prey may be interpreted as suggestive of changes in the overall ecosystem biodiversity
(but see also Cabeza et al. 2008). Clearly, importance
of these changes for individual owl species will vary,
depending on the strength of its dependence on typically grassland prey. However, the net effect for all the
species considered should be probably negative. Farmland in Poland, as well as in other countries of Central and Eastern Europe, is undergoing further changes,
with continuing loss of grassland areas. If these changes
are associated with decline and ultimate loss of important, once widespread predators, as we show here, the
resultant loss of biodiversity may be even more pronounced due to the cascading effects connected with
removal of top predators in ecosystems (Terborgh &
Estes 2010).
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Densities of owls in Polish farmland
Acknowledgements
We are grateful to Dr. P. Matyjasiak and to two anonymous
referees for valuable comments on the manuscript and M.
Sikora for technical assistance. J. Kubacka and B. Przybylska kindly improved the English.
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Received February 29, 2012
Accepted July 26, 2012
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