Population densities and habitat use of the golden jackal (Canis aureus) in farmlands across the Balkan Peninsula
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.
KeywordsGolden jackal Acoustic monitoring Population density Habitat use GIS Farmland Balkan Peninsula
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
Habitat composition (in percent) in individual study areas of golden jackal monitoring
Heterogeneous agricultural areas
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.
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.
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).
Summary of the results of the golden jackal monitoring in studied countries
Number of calling stations
Number of groups
Population density (groups/10 km2)
Correlations between types of habitat and first and second canonical axes (Monte Carlo permutation tests) in the study countries
Heterogeneous agricultural areas
The effect of factors to habitat preferences of golden jackal
Explained variability (%)
Agricultural intensity × country
Number of groups
Agricultural intensity × country
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.
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).
- Banea OC, Krofel M, Červinka J, Gargarea P, Szabó L (2012) New records, first estimates of densities and questions of applied ecology for jackals in Danube Delta Biosphere Reserve and hunting terrains from Romania. Acta Zool Bulg 64:353–366Google Scholar
- CORINE 2006 seamless vector dataset. http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-2
- Ćirović D, Milenković M, Paunović M, Penezić A (2008) Present distribution and factors of range spread of golden jackal (Canis aureus L. 1758) in Serbia. In: Hunting Association of Serbia (Ed.), Proceedings of the International Conference on large carnivores, Žagubica, pp. 93–102 (In Serbian with English summary)Google Scholar
- ESRI (2011) ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research InstituteGoogle Scholar
- Giannatos G (2004) Conservation action plan for the golden jackal Canis aureus L. in Greece. WWF Greece, AthensGoogle Scholar
- Giannatos G, Marinos Y, Maragou P, Catsasorakis G (2005) The golden jackal (Canis aureaus L.) in Greece. Belg J Zool 135:145–149Google Scholar
- Jhala YV, Moehlman PD (2004) Golden jackal Canis aureus. In: Sillero-Zubiri C, Hoffmann M, Macdonald D (eds) Canids: Foxes, Wolves. Jackals and Dogs Status Survey and Conservation Action Plan. IUCN/SSC Canid Specialist Group Gland, Switzerland, pp 156–161Google Scholar
- Krofel M (2008) Survey of golden jackals (Canis aureus L.) in Northern Dalmatia, Croatia: preliminary results. Natura Croatica 17:259–264Google Scholar
- Markov G, Lanszki J (2012) Diet composition of the golden jackal Canis aureus in an agricultural environment. Folia Zool 61:44–48Google Scholar
- Python Software Foundation (2012) Python Language Reference, version 2.6. http://www.python.org. Accessed Aug 2012
- QGIS (2012) Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org
- R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org. ISBN 3-900051-07-0Google Scholar
- Sillero-Zubiri C, Reynolds J, Novaro A (2004) Management and control of canids near people. In: Macdonald DW, Sillero-Zubiri C (eds) Biology and conservation of wild canids. Oxford University Press, Oxford, pp 17–40Google Scholar
- Simeneh G (2010) Habitat use and diet of golden jackal (Canis aureus) and human-carnivore conflict in Guassa community conservation area, Menz. MSc.Thesis, Addis Ababa University. Addis AbabaGoogle Scholar
- StatSoft Inc (2010) STATISTICA (data analysis software system), version 9.1. www.statsoft.com
- Stoyanov S (2012) Golden jackal (Canis aureus) in Bulgaria. Current status, distribution, demography and diet. International symposium on hunting, “Мodern aspects of sustainable management of game population” Zemun-Belgrade, Serbia, 22–24 June, 2012. pp 48–56Google Scholar
- Szabó L, Heltai M, Lanszki J, Szűcs E (2007) An indigenous predator, the golden jackal (Canis aureus L. 1758) spreading like an invasive species in Hungary. Bulletin USAMV-CN, 63–64Google Scholar
- ter Braak CJF, Šmilauer P (1998) CANOCO release 4. Reference manual and user's guide to Canoco for Windows: software for canonical community ordination. Microcomputer Power, IthacaGoogle Scholar