European Journal of Wildlife Research

, Volume 60, Issue 2, pp 193–200 | Cite as

Population densities and habitat use of the golden jackal (Canis aureus) in farmlands across the Balkan Peninsula

  • Martin Šálek
  • Jaroslav Červinka
  • Ovidiu C. Banea
  • Miha Krofel
  • Duško Ćirović
  • Ivana Selanec
  • Aleksandra Penezić
  • Stanislav Grill
  • Jan Riegert
Original Paper

Abstract

The main objective of this study was to analyze the habitat use and population densities of the golden jackal in four countries across lowland regions of the Balkan Peninsula, known as the core area of the species' distribution in Europe. Using indirect (acoustic) method for detecting territorial golden jackals, we surveyed jackal presence and densities on 331 monitoring sites in four countries, covering area an of 4,296 km2 in total during April and May 2007–2012. We used GIS to assess landscape and environmental characteristics in a 2-km circular buffer (12.6 km2) around calling stations. Average population density of golden jackals in the study areas ranged between 0.6 and 1.1 territorial groups/10 km2 (mean ± SE, 0.6 ± 0.06 groups/10 km2), with several high-density areas with up to 4.8 territorial groups/10 km2. Analysis of habitat use showed that for both jackal occurrence and number of jackal groups, the only significant parameter was the interaction between country and intensity of agriculture, indicating that jackals adapt their habitat selection patterns in relation to the habitat availability. We observed that selection of the more suitable habitats (shrub–herbaceous vegetation/heterogeneous agricultural vegetation) increased with lower proportion of these habitat types in the study area. Our study confirms high habitat plasticity of the golden jackal and offers explanation for its recent range expansion, which might be connected with the land use changes during the last decades in the Balkan Peninsula.

Keywords

Golden jackal Acoustic monitoring Population density Habitat use GIS Farmland Balkan Peninsula 

Introduction

The golden jackal (Canis aureus) is a territorial, widely distributed medium-sized generalist carnivore, occurring in northeastern Africa, South Asia, and southeastern and central Europe (Jhala and Moehlman 2004). In Europe, the golden jackal populations changed significantly during the twentieth century, presumably due to agricultural intensification, habitat loss, and past extermination programs (Kryštufek et al. 1997; Arnold et al. 2012). During the first half of the twentieth century, the golden jackal population numbers declined dramatically, which led to several local extinctions and its distribution became highly fragmented (Kryštufek et al. 1997). Following protection in core areas of the Balkan Peninsula, the number of jackals increased in the second half of the last century and currently, its populations are scattered mainly along the Eastern Mediterranean and Black Sea, with recent rapid spread to western and Central Europe (Arnold et al. 2012).

Due to their habitat plasticity and opportunistic feeding habits, golden jackals can live in a wide variety of natural and/or seminatural habitats, including human-dominated agricultural landscapes. According to the available knowledge of its distribution in Europe, jackals are mainly associated with shrub vegetation and mosaic of cultural landscape and lowland wetlands (Giannatos 2004; Sillero-Zubiri et al. 2004). Dense shrub vegetation is considered an important habitat, which provides adequate resource availability, such as prey and den sites, as well as better cover to avoid persecution by people (Giannatos 2004). However, information on the habitat selection of golden jackals in Europe is still very limited and according to our knowledge, no study is available to better understand habitat selection on a large geographical scale.

The main goal of this study was to assess the population densities and habitat use of the golden jackal during the breeding season in four countries across its distribution range in Eastern and Southeastern Europe. In particular, our analysis was focused on habitat use and factors affecting population densities. Our results bring the first detailed information about the golden jackals' habitat use, which could be used for local landscape planning, assessing conservation priorities, and development of management plans.

Material and methods

Study areas

To compare habitat use and population densities of the golden jackal across lowland regions with different landscape patterns and land use composition, we selected four countries in the Balkan Peninsula: (1) Bulgaria (size of study area, 2,864 km2), (2) Serbia (565 km2), (3) Croatia (452 km2), and (4) Romania (415 km2). The climate differs considerably among selected regions. In general, the study region in southern Croatia is mainly influenced by Mediterranean climate, with hot summers and low precipitations (150 mm) and the rest of the areas exhibit a combination of continental and Mediterranean climate with higher precipitations during summer months (270–698 mm). The study areas are characterized by predominantly flat or gently rolling relief with altitudes ranging between 8 and 505 (mean = 179) m a.s.l. The agricultural vegetation is dominated by a mosaic of crops (mainly cereals and corns), vineyards, and grasslands (Table 1). The grasslands are comprised of a variety of types including meadows, xerophytic, and mesophytic grasslands and extensively used pastures (mainly for sheep and cattle).
Table 1

Habitat composition (in percent) in individual study areas of golden jackal monitoring

 

Bulgaria

Serbia

Croatia

Romania

Arable

46.0

47.6

9.6

55.4

Forest

15.0

14.2

13.5

19.8

Heterogeneous agricultural areas

3.7

12.7

27.4

5.5

Pastures

6.2

0.8

8.5

4.6

Shrub–herbaceous vegetation

21.9

15.1

31.7

4.4

Urban areas

7.0

4.9

5.9

6.9

Water bodies

0.3

4.7

3.5

3.5

Study design

The occurrence of golden jackals was monitored using acoustic monitoring at linear transects that consisted of five calling stations. To achieve geographical independence of the sites, the distance between neighboring calling stations was 4 km (total length = 20 km). Two kilometers are maximum human hearing distance on windless nights from vantage point in an open terrain with no background noise (Giannatos et al. 2005; Szabó et al. 2007). Moreover, the transect method was supplemented with jackal monitoring on individual calling stations not included (contained) in transects, however, we still controlled for minimal distance criterion between individual monitoring sites. The study areas were chosen prior to the field work, based on digitalized aerial maps (1:5,000) using a geographical information system (ESRI 2011; QGIS 2012) and previous information about jackal presence. Position of the calling stations was adjusted according to the topographical characteristics, infrastructure, and human settlements in order to optimize sound transmission.

Golden jackal monitoring

The acoustic monitoring is an effective and widely used method to determine the distribution patterns and occurrence of social and vocal territorial canids (Harrington and Mech 1982). Similarly, this method has been recently successfully used for golden jackal monitoring in Europe (Giannatos et al. 2005; Krofel 2008). The localities were visited once during early breeding period (April–May; see also Szabó et al. 2007; Krofel 2008) when jackal activity was expected to be concentrated mainly to the core area inside the territory around their dens (Jhala and Moehlman 2004). Monitoring was conducted during the highest golden jackal vocal activity (1 h after sunset till midnight; Giannatos et al. 2005; Krofel 2008), with favorable meteorological conditions (no rain or strong wind). The acoustic monitoring was based on the broadcasting of jackal howling, which stimulated the jackals to respond and thus enabled identification of the accurate position of territorial animals. We used 50 Watt Etrion MR 800gr and 100-W Monacor PA-302 Megaphone connected with mobile phone, MP3 player, or portable computer. At the calling station, the recorded yip-howl of three to four golden jackals was broadcasted for 30 s followed by a 5-min pause. This set of broadcast and pause was repeated until jackals responded a maximum of five times at each calling station giving a total maximum time of approximately 30 min per station. For each calling station, we recorded geographical coordinates and altitude by GPS device, as well as the number of howling jackals (single or group) and number of groups. In order to avoid double counting of two neighboring groups, we used hand-operated compass to determine an accurate direction (azimuth) of each howling group. It was assumed that each response direction coincides with a territorial group (Giannatos et al. 2005). In addition, this method was supplemented by direct observation of the golden jackals in the surrounding of the calling stations using spotlights or night vision goggles by detecting animals by eye shine.

GIS analysis

Habitat use of the golden jackal were studied using comparison of occupied versus unoccupied localities, which is a standard approach used in habitat selection studies (Huck et al. 2011). To reveal the environmental factors that affect golden jackal occurrence, we investigated main habitat and environmental characteristics in a 2-km buffer zone (12.6 km2) around each monitoring site. The size of the buffer zones (sample plots) was derived as an equivalent to the average home range size from radio-tracked animals (Giannatos 2004). We identified six land use characteristics that were reported as important spatial predictors of jackal occurrence (Giannatos et al. 2005; Simeneh 2010). In particular, we assessed proportion of each of these six habitat types as follows: forest, arable land, heterogeneous agricultural vegetation, shrub–herbaceous vegetation, pastures, urban area, and water bodies (for detailed description, see Appendix 1). Land use variables were obtained from 1:100,000 Corine Land Cover maps (Corine 2006), that were updated using actual aerial maps (Google Maps, 2012) using GIS tools (ESRI 2011; QGIS 2012). Landscape metrics were calculated within ESRI environment with batch processing in script for individual calling station (ESRI 2011; Python Software Foundation 2012). Elevation for individual calling station was derived from global GMTED raster dataset.

Statistical analyses

To avoid autocorrelations of percentage data on habitat proportions, we performed principal component analysis (PCA) in CANOCO software (ter Braak and Šmilauer 1998) with log-transformed values. We also calculated correlations of each habitat variable with first and second canonical axis using Monte Carlo permutation tests. For subsequent analysis, we used scores from these axes. Habitat use was assessed by generalized linear mixed model (GLMM) using R software v. 2.8.1 (R Development Core Team 2009). We used lmer function to develop null models with transect as a random factor and (1) golden jackal occurrence (0/1) and (2) number of jackal groups as dependent variables. A binomial error distribution was used for presence (0/1) and a Poisson distribution was used for the number of jackal groups detected per calling station. Independent variables included elevation (in meters above sea level), country, agricultural intensity (score from first canonical axis) and gradient between urban and forested areas (score from the second canonical axis). We also tested the effect of interactions among these factors. We used forward selection based on AIC criterion to build the best model for each dependent variable. Particular differences in habitat proportions for each country were analyzed using Mann–Whitney U test (presence/absence) and single regression (number of groups) using Statistica v. 9.1 (StatSoft 2010).

Results

Population status

In total, we monitored the presence of golden jackal and population densities from 331 calling stations (covering a total area of 4,296 km2; Table 2). The presence of 266 golden jackal groups was recorded at 147 monitoring sites (44 %). The proportion of occupied sites in different countries ranged from 9 to 67 % in Romania and Serbia, respectively. Mean density in individual countries was 0.6 groups/10 km2 (0.1–1.1 groups/10 km2; Table 2); however, locally, we recorded up to 4.8 groups/10 km2 (∼6 groups from single calling station). The number of territorial groups on individual calling station in the studied countries is presented in Appendix 2.
Table 2

Summary of the results of the golden jackal monitoring in studied countries

 

Area (km2)

Number of calling stations

Occupied (%)

Number of groups

Population density (groups/10 km2)

Bulgaria

2,864

217

95 (44)

160

0.6

Serbia

565

45

30 (67)

63

1.1

Croatia

452

36

19 (53)

40

0.9

Romania

415

33

3 (9)

3

0.1

Total

4,296

331

147 (44)

266

0.6

Habitat use

The first two canonical axes in PCA explained together 42.8 % of data variability. Proportions of arable areas, heterogeneous agricultural areas, and shrub–herbaceous vegetation were correlated with the first canonical axis. Arable areas showed negative correlation and heterogeneous agricultural areas and shrub–herbaceous vegetation showed positive relationship with jackal presence. The first canonical axis in PCA expressed the intensity of agriculture (with increasing score, agricultural intensity decreases). Proportion of forests and urban areas were correlated with the second canonical axis (a higher score on the second canonical axis indicates a higher proportion of urban areas and a lower proportion of forests; Table 3).
Table 3

Correlations between types of habitat and first and second canonical axes (Monte Carlo permutation tests) in the study countries

  

Correlation coefficient

Gradient

Habitat type

First axis

Second axis

Agricultural intensity

Arable

−0.884

0.137

Heterogeneous agricultural areas

0.675

0.461

Shrub–herbaceous vegetation

0.494

−0.224

Forest/urban area

Forests

0.255

−0.710

Urban area

0.093

0.528

Relationships with statistical significance below P = 0.05 are in italics

In both models, with jackal presence/absence or with number of jackal groups, the only significant parameter was the interaction between overall agricultural intensity (expressed by scores on first ordination axis) and country (Table 4). Results of binomial model on the presence/absence of jackals showed that the differences were significant only for Croatia and Serbia, the two countries showing opposite trends (Appendix 3; Mann–Whitney U tests; Croatia U = 88.0, P = 0.02071; Serbia U = 85.0, P = 0.00078). In Croatia, the occupied sites comprised significantly higher proportion of arable areas than unoccupied sites (Appendix 5). In Serbia, occupied sites showed significantly lower proportion of arable areas and significantly higher proportion of heterogeneous agricultural area and shrub–herbaceous vegetation than unoccupied sites. Higher proportion of shrubs at occupied sites compared to unoccupied ones was found also in Bulgaria (Appendix 5). Number of groups was significantly affected by overall agricultural intensity only in Serbia (Appendix 4), single regression; beta = 0.527, P = 0.00002. In particular, the number of groups increased with increasing proportion of extensive agricultural sites (shrub–herbaceous vegetation and heterogeneous agricultural areas) and decreased with increasing proportion of arable areas. We also found a significant positive relationship between proportions of shrub–herbaceous vegetation and number of groups in Bulgaria (Appendix 5).
Table 4

The effect of factors to habitat preferences of golden jackal

Dependent variable

Distribution

Independent variable

d.f.

Explained variability (%)

P value

Presence/absence

Binomial

Agricultural intensity × country

9

7.8

0.00002

Number of groups

Poisson

Agricultural intensity × country

9

6.5

0.00017

Results of GLMM models

Discussion

Our results bring the first complex data on population densities and habitat use of the golden jackal from several regions across the Balkan Peninsula. The highest population density was found in the lowlands around Danube River in Serbia, where an average density of 1.1 groups/10 km2 was recorded. This area has been described as the core area of jackal distribution in Serbia (Ćirović et al. 2008). In contrast, the average density of only 0.1 groups/10 km2 was recorded in the agriculture-dominated landscape in southeastern Romania. We assume that the differences in population densities across studied regions could be associated with different agricultural management and land use intensity, such as proportion of shrub–herbaceous and heterogeneous agricultural vegetation, which were noted as the main predictors of golden jackal occurrence (see below). For example, in Romania, in the study area with the lowest proportion of preferred habitats, there was also the lowest density of jackal groups. Differences in densities are probably also in connection with food availability (especially anthropogenic food sources) and management practices (especially intensity of human persecution), i.e., parameters that were not included in our analysis. The results of the average group density from our regions were comparable with other studies from various parts of Romania (Banea et al. 2012), Hungary (Szabó et al. 2007), and Greece (Giannatos et al. 2005). Finally, in concordance with previous studies (Giannatos 2005; Szabó et al. 2007), our results also found locally high-density areas where population densities reached up to 4.8 groups/10 km2.

Although golden jackals are known as habitat generalists that inhabit a variety of different habitat types (Giannatos 2004; Jhala and Moehlman 2004; Stoyanov 2012), our data showed that the key factor among habitat characteristics affecting their occurrence in human-dominated landscapes was the intensity of agricultural management. These results were identical for both types of analyses. The golden jackal occurrence probability increased in localities with higher proportion of shrubs–herbaceous vegetation and heterogeneous agricultural vegetation and decreased with increasing proportion of arable area. Different types of shrub and extensively used heterogeneous agricultural habitats (consisting of woody shrubs and herbaceous vegetation) are structurally highly diverse and provide a suitable environment for jackals. These habitats offer cover and serve for protection against human hunting pressure (Giannatos 2004; Simeneh 2010). Shrub vegetation and extensively used agricultural areas have been previously reported as important habitats for small mammals (e.g., Alain et al. 2006), which are known as a primary food for golden jackals in Europe (Lanszki et al. 2009; Markov and Lanszki 2012; Stoyanov 2012). Additionally, various shrub species provide a seasonal source of fruits and berries, which also comprise a large part of the golden jackal diet (Markov and Lanszki 2012). Therefore, we assume that habitats where we observed higher probability of jackal occurrence also offer higher food availability for jackals.

Our results showed significant differences in habitat use among individual study areas. This result indicates that selection patterns may differ due to geographical and regional variations in agricultural intensity and land use composition. In Croatia, the landscape is characterized by extensive agricultural use and small proportion of arable habitats (9 % arable land). In contrast, Serbian study sites comprised large proportions of arable area (48 % arable land). Golden jackals selected the extensive and non-agricultural habitats (shrub–herbaceous vegetation and heterogeneous agricultural vegetation) in the landscapes where intensive agricultural management predominated, while significant selection for intensive agricultural habitats (arable land) was found in the area with predominantly extensive agricultural use. In intensively used agricultural landscapes with high proportion of arable land, the importance of edge habitats has been repeatedly stressed as the crucial structure for carnivore persistence (Šálek et al. 2009, 2010; Červinka et al. 2011; 2013). Our results indicate remarkable habitat plasticity of the golden jackal throughout its distribution range and highlight area-specific jackal use across its distribution range.

According to our results, it seems possible that population increase of golden jackals in Eastern European farmlands could be partly connected to land abandonment and depopulation of traditional rural regions (Kuemmerle et al. 2009; Müller et al. 2009), which led to the abandonment of arable land and other intensively used habitats, resulting in an increase in the proportion of shrub vegetation and habitat edges. Results from jackal habitat use presented in this study indicate that shrub vegetation and abandoned farmlands present the most suitable habitat for golden jackal. Thus, these land use changes and human emigration from rural areas appear beneficial for the golden jackal, and probably also several other generalist species.

Notes

Acknowledgments

We are grateful to Aleš Lipičnik, Nadja Osojnik, and Aleš Sedlar for their help in the field. We thank Giorgos Giannatos, Nikolai Spassov, Luca Lapini, Stoyan Stoyanov, and Dumitru Murariu for their scientific and technical advice. We would also like to thank Marina Kipson for editing the English manuscript. This work was supported by the research aim of the Academy of Sciences of the Czech Republic (RVO 68081766), by the grant of Grant Agency of University of South Bohemia 168/2013/P and Ministry of Education and Science of the Republic of Serbia (Contract TR 31009).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Martin Šálek
    • 1
  • Jaroslav Červinka
    • 2
    • 3
  • Ovidiu C. Banea
    • 4
  • Miha Krofel
    • 5
  • Duško Ćirović
    • 6
  • Ivana Selanec
    • 7
  • Aleksandra Penezić
    • 6
  • Stanislav Grill
    • 8
  • Jan Riegert
    • 9
  1. 1.Institute of Vertebrate BiologyAcademy of Sciences of the Czech RepublicBrnoCzech Republic
  2. 2.Faculty of Agriculture, Applied Ecology LaboratoryUniversity of South BohemiaČeské BudějoviceCzech Republic
  3. 3.Administration of Třeboňsko PLANature Conservation Agency of the Czech RepublicTřeboňCzech Republic
  4. 4.Ecology Department of Crispus NGO SibiuSibiuRomania
  5. 5.Biotechnical FacultyUniversity of LjubljanaLjubljanaSlovenia
  6. 6.University of BelgradeFaculty of BiologyBelgradeSerbia
  7. 7.Association BIOMZagrebCroatia
  8. 8.Department of Ecosystem Biology, Faculty of ScienceUniversity of South BohemiaČeské BudějoviceCzech Republic
  9. 9.Department of Zoology, Faculty of ScienceUniversity of South BohemiaČeské BudějoviceCzech Republic

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