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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 Unauthenticated Download Date | 6/14/17 9:43 PM Densities of owls in Polish farmland 1205 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 Unauthenticated Download Date | 6/14/17 9:43 PM M. Żmihorski et al. 1206 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 Unauthenticated Download Date | 6/14/17 9:43 PM Densities of owls in Polish farmland 1207 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 Unauthenticated Download Date | 6/14/17 9:43 PM 1208 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). Unauthenticated Download Date | 6/14/17 9:43 PM 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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Habitat preferences of a declining population of the little owl, Athene noctua in Central Poland. Folia Zool. 58 (2): 207–215. Received February 29, 2012 Accepted July 26, 2012 Unauthenticated Download Date | 6/14/17 9:43 PM