Habitat Suitability Model for the ( aureus) in Venezia Giulia and Analysis of Possible Interaction with the Grey (Canis lupus) and the Red Fox (Vulpes vulpes)

A GIS Based Modelling Approach

Ursula Maria STERRER, BSc BSc

01017343

Innsbruck, December 2019

Master Thesis

Submitted at the Leopold Franzens University Innsbruck,

Faculty of Biology for achieving the academic degree Master of Science

Supervised by:

Dr. Johannes Rüdisser

Institute of Ecology at the Leopold Franzens University Innsbruck

&

Stefano Filacorda, PhD

Faculty of Science and Technology at the Free University of Bozen/Bolzano

ACKNOWLEDGEMENTS

First, I would like to thank my supervisors, Dr. Johannes Rüdisser and Stefano Filacorda, PhD for helping me with this master thesis. Special thanks to Johannes Rüdisser, who helped me with questions concerning my work in GIS. It was also a truly great experience to be able to collect at least part of the data myself in with Stefano Filacorda. Also thank you to all the people involved in the research being conducted in and for letting me use all the data you collected over the last decade, without which this thesis would not be what it is now. I want to thank my family, that has supported me throughout my whole university career and who never fail to give me great advice. I would also like to thank Florin Kunz, who was always there for me and gave me the support I needed in times, when finishing the master thesis seemed far away. Thank you for listening to my struggles and for our great discussions. Thanks to all my fellow EMMA’s who made the last years so great, and especially to Kat, for reading and reviewing this thesis.

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ABSTRACT

Modelling species distribution and understanding the effects that a guild of predators has on each other is of great interest not only from an ecological point of view but also for management decisions. Golden jackals (Canis aureus) have been expanding their range from eastern Europe to the west and successfully established populations in Friuli Venezia Giulia. The grey wolf (Canis lupus) has made its return to the region recently with the first evidence of reproduction in 2018 in the area of Magredi. The red fox (Vulpes vulpes) is highly abundant in Friuli Venezia Giulia and the effects the larger predators could have on this are still unknown. This study aims to model suitable habitat for the golden jackal to gain more information of this species’ habitat use and altitudinal preferences. Furthermore, I want to investigate possible effects that the three canids could have on each other by interpreting the presence records and camera trap pictures. I modeled the habitat suitability with Maxent. Therefore, we collected presence data on all large and medium-sized occurring in the region with three different methods. In 2019, we added data to an existing dataset (from 2010-2018) of acoustic stimulation surveys of golden jackals. Additionally, we collected presence data through snow & mud tracking and through opportunistically placed camera traps. The habitat suitability model showed, that golden jackals prefer habitats along rivers, forests in the colline and montane altitudinal belt and extensively used agricultural fields. The sandy gravel banks along rivers seem to be especially important. The habitat suitability model indicates, that large parts of the region, especially in the plains in the middle part of the region and the northern mountain valleys, are suitable for the golden jackal. Golden jackals were found in significantly lower altitudes than grey and red foxes respectively. In the region, where reproductive grey wolves are present in the lowlands, the golden jackals stopped answering to acoustic stimulation which could be a sign of intraguild competition (Lapini et al. 2018). From the camera traps, I was able to analyze the habitat use of golden jackals and red foxes in one specific case. Although it is not enough data for statistical analysis, it seems as if red foxes choose to be present in the habitat on different days than the golden jackals, therefore engaging in a behavior of avoidance. Further research is required to get more in depth information on the behavior of the three species.

III

Nicht nur aus einem ökologischen Blickwinkel, sondern auch für Entscheidungen im Management von Arten, ist es wichtig, Kenntnis über die Artverteilung und Interaktion von Beutegreifern zu erlangen. Der Goldschakal (Canis aureus) hat sein Territorium ausgehend von Ost-Europa erfolgreich in den Westen ausgedehnt und Populationen haben sich auch in Friaul- Julisch-Venetien angesiedelt. Der Wolf (Canis lupus) ist in der jüngsten Zeit ebenfalls in die Region zurückgekehrt und 2018 gab es in Magredi den ersten Reproduktionsnachweis. Der Fuchs (Vulpes vulpes) ist in der Region zahlreich vorhanden, allerdings sind die Effekte, die die größeren Raubtiere auf die Art haben noch weitgehend unbekannt. Diese Arbeit versucht ein Modell geeigneter Lebensräume für den Goldschakal zu erstellen, um mehr über den Lebensraum und die Höhenverteilung der Art zu erfahren. Zusätzlich soll die Interaktion der drei Caniden durch Interpretation von Artnachweise und Fotofallen Bildern erforscht werden. Der geeignete Lebensraum wurde mit Maxent modelliert. Dazu wurden Artnachweise für alle Säugetiere in der Region mit drei verschiedenen Methoden gesammelt. Zu einem existierenden Datensatz (2010-2018) wurden für den Goldschakal 2019 neue Daten durch akustische Stimulation hinzugefügt. Außerdem wurden Artnachweise durch Snow & mud tracking und Fotofallen gesammelt. Der geeignete Lebensraum für Goldschakale scheint vor allem entlang von Flussläufen, in Wäldern der collinen und montanen Höhenstufe und in Gebieten extensiver Landwirtschaft zu finden zu sein. Dabei scheinen die sandigen Schotterbänke entlang von Flüssen von außerordentlicher Bedeutung zu sein. Das Modell zeigt, dass große Teile der Region, vor allem im Flachland in der Mitte der Region und in den Bergtälern des Nordens geeignetes Habitat für den Goldschakal bereitstellen. Goldschakale wurden in signifikant niedrigeren Lagen gefunden als Wölfe und Füchse. Dort wo es reproduktive Wölfe in der Region gibt, haben die Goldschakale aufgehört auf die akustische Stimulation zu antworten, was ein Zeichen für Konkurrenz sein kann (Lapini et al. 2018). Die Ergebnisse der Fotofallen lassen nur eine Analyse in einem spezifischen Fall zu. Obwohl der Datensatz sehr klein ist, konnte die Tendenz gezeigt werden, dass Füchse zwar das gleiche Habitat nutzen, den Goldschakalen aber aus dem Weg gehen indem sie an unterschiedlichen Tagen anwesend sind. Weitere Studien sind nötig, um die Interaktion der drei Arten besser zu erforschen.

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TABLE OF CONTENT

Acknowledgements ...... II Abstract ...... III Table of Content ...... V 1 Introduction ...... 1 1.1 Species Distribution Models (SDM) ...... 1 1.2 Carnivore Interaction ...... 1 1.3 Research Question ...... 4 1.4 Description of the Target Species ...... 4 1.4.1 The Golden Jackal Canis aureus L., 1758 ...... 4 1.4.2 The Grey Wolf Canis lupus L., 1758 ...... 8 1.4.3 The Red Fox Vulpes vulpes L., 1758 ...... 10 2 Material and Methods ...... 12 2.1 Study Area...... 12 2.1.1 Geography ...... 12 2.1.2 MA1: Cansiglio ...... 13 2.1.3 MA2: Magredi ...... 13 2.1.4 MA3: Carnia/Upper Tagliamento ...... 14 2.1.5 MA4: Middle Tagliamento ...... 14 2.1.6 MA5: Lower Tagliamento ...... 14 2.1.7 MA6: Val Aupa/Glazzat ...... 15 2.1.8 MA7: Julian Pre-Alps/Natisone ...... 15 2.1.9 MA8: Torre ...... 15 2.1.10 MA9: Goritian Karst & MA10: Karst ...... 15 2.2 Field Surveys ...... 15 2.2.1 Snow & Mud Tracking ...... 15 2.2.2 Acoustic Stimulation ...... 17 2.2.3 Camera Trapping ...... 19 2.3 Maxent-Data ...... 20 2.3.1 Presence Points ...... 20 2.3.2 Environmental Factors ...... 20 2.3.3 Digital Elevation Model (DEM) ...... 21 2.3.4 Precipitation and Temperature ...... 22 2.3.5 Corine Land Cover (CLC) ...... 22 2.3.6 Corine Biotopes Habitats ...... 22 V

2.3.7 Forest Typology ...... 22 2.3.8 River Network ...... 22 2.3.9 Snow Cover Duration (SCD) ...... 22 2.3.10 Terrain Ruggedness Index (TRI) ...... 23 2.3.11 Natura 2000 Areas ...... 23 2.4 Maxent-Settings...... 23 2.4.1 Background Data ...... 24 2.4.2 Features ...... 24 2.4.3 Regularization ...... 24 2.4.4 Sampling Bias ...... 24 2.4.5 Model Output ...... 26 2.4.6 Interpretation of the output ...... 26 3 Results ...... 28 3.1 Snow & Mud Tracking ...... 28 3.2 Acoustic Stimulation ...... 30 3.3 Camera Trapping ...... 32 3.4 The Habitat Suitability Model for the Golden Jackal ...... 35 3.4.1 Influence of Environmental Factors ...... 41 3.4.2 Possible Golden Jackal Density in Friuli Venezia Giulia ...... 52 3.5 Signs of Interaction of the Target Species ...... 54 4 Discussion ...... 55 4.1 Habitat Suitability Model for the Golden Jackal ...... 55 4.1.1 Methodological Critique ...... 55 4.1.2 Critique of the Results ...... 56 4.2 Interaction of Grey Wolf, Golden Jackal and Red Fox ...... 59 5 Conclusion ...... 62 6 References ...... 63 7 Annex ...... 73 8 Final Appendix ...... 94

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List of Figures Figure 1: Map of the macro areas in Friuli Venezia Giulia chosen for the snow & mud tracking survey and the acoustic stimulation survey adapted from Comazzi et al. (2016). Each macro area is labelled by a code: MA1 (Cansiglio), MA2 (Magredi), MA3 (Carnia/Upper Tagliamento), MA4 (Middle Tagliamento), MA5 (Lower Tagliamento), MA6 (Val Aupa/Glazzat), MA7 (Julian Pre-Alps/Natisone), MA8 (Torre), MA9 (Goritian Karst) MA10 (Trieste Karst)...... 13 Figure 2: Examples of a typical environment of MA2: Magredi (left) in the lowlands near Pordenone and MA5: lower Tagliamento (right)...... 14 Figure 3: Snow & mud tracking locations in the region of Friuli Venezia Giulia. The blue asterisks represent snow & mud tracking locations of the season 2019. White points locate the major cities of Friuli-Venezia-Giulia for better orientation, blue lines represent the major rivers of the region...... 17 Figure 4: Examples for the signs of presence documented in the snow & mud tracking survey. Left a footprint of a golden jackal can be seen, right the scat of a grey wolf is depicted...... 17 Figure 5: Frequency modulation in canid howls with the example of the golden jackal and the European grey wolf (modified from Kershenbaum et al. 2016)...... 18 Figure 6: Target Group Background: sample effort depicted in the region of Friuli Venezia Giulia. The warmer the colors, the more samples have been taken for the golden jackal in this area...... 26 Figure 7: This boxplot represents the distribution in altitude of the golden jackal (blue; median = 405.6 m), the red fox (orange; median = 1004.4 m) and the grey wolf (grey; median = 997.3 m) from the snow & mud tracking data...... 29 Figure 8: Distribution of the calling stations used in the acoustic stimulation survey (2010- 2019). Yellow dots indicate calling stations where golden jackals responded, blue dots indicate calling stations without response by golden jackals. Not all calling stations were visited every year. This dataset represents the cumulative data from all ...... 31 Figure 9: Number of responses of golden jackals relative to the number of emissions in percent from 2010-2019. Overall represents the mean of all years...... 31 Figure 10: Number of records of golden jackal (blue) and red fox (orange) on a camera trap along the Torre river in MA8. The x-axis corresponds to the specific date of the record. This graph represents the daily activity of the golden jackal and the red fox...... 33 Figure 11: Two golden jackals photographed by a camera trap along the river Torre in MA8...... 33 Figure 12: Number of records of the golden jackal (blue) and the red fox (orange) on two camera traps placed approximately 15 m apart in MA3 along the Tagliamento river. The x-axis corresponds to the specific date of the record. This graph represents the daily activity of the golden jackal and the red fox...... 34 Figure 13: Diurnal activity of the golden jackal (blue) and the red fox (orange) pooled from all three camera traps (two from MA3 along the Tagliamento river and one from MA8 along the Torre river)...... 34

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Figure 14: Habitat suitability model derived from the median of the 15-fold cross validated logistic output of Maxent for the golden jackal in Friuli Venezia Giulia. The more intense the color is displayed, the higher is the habitat suitability...... 36 Figure 15: Habitat suitability model and presence records of golden jackals in Friuli Venezia Giulia. With more color intensity the habitat suitability increases. Yellow points indicate presence records of golden jackals from the snow & mud tracking and acoustic stimulation surveys...... 37 Figure 16: Habitat suitability and presence records of the grey wolf (green), the red fox (blue) and the golden jackal (yellow)...... 38 Figure 17: Composition of suitable habitat over a habitat suitability value of 0.5 and its share in area classified by Corine Land Cover in Friuli Venezia Giulia. Only classes with a minimum area of 0.1 km² were included in the analysis...... 39 Figure 18: Threshold dependent test on model performance by evaluating omission and predicted area of the habitat suitability model of the golden jackal. The blue line represents the mean omission on test data with standard errors (yellow)...... 40 Figure 19: ROC curve of the habitat suitability model of the golden jackal. The red line shows the mean AUC with standard errors (blue). The black line shows the random prediction. .... 41 Figure 20: Response curve for Terrain Ruggedness Index (TRI) for the habitat suitability model for the golden jackal. The blue line corresponds to the TRI being the only variable and the orange line corresponds to the influence of the TRI on the whole model...... 47 Figure 21: Response curve for DEM for the habitat suitability model of the golden jackal. The blue line corresponds to the DEM being the only variable and the orange line corresponds to the influence of the DEM on the whole model...... 47 Figure 22: Natura 2000 areas with areas of high habitat suitability >0.5 (blue) and Natura 2000 areas with areas of low habitat suitability (black) in Friuli Venezia Giulia...... 51 Figure 23: Interaction of the grey wolf, the golden jackal and the red fox in Friuli Venezia Giulia derived from the snow & mud tracking dataset on a 3 x 3 km grid (dark green = grey wolf and golden jackal; light green = grey wolf and red fox; orange = golden jackal and red fox; red = grey wolf, golden jackal and red fox)...... 54 Figure 24: Terrain Ruggedness Index for Friuli Venezia Giulia calculated from the DEM with qGIS ...... 73 Figure 25: Jackknife tests for training and testing gain as well as AUC of each environmental factor of the habitat suitability model for the golden jackal. Light blue bars show model gain without the variable, dark blue bars show model gain with each environmental factor as only variable. The red bar at the bottom shows the overall gain of the model. Note that the scale of the x-axis is different in all three plots...... 73 Figure 26: Response curve of the habitat suitability model of the golden jackal with average annual temperature, average annual precipitation, distance to rivers and average annual snow cover duration. The orange lines/bars correspond to the habitat suitability values of the respective environmental factors from the overall model. The blue lines/bars correspond to each environmental factor being the only variable...... 93

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List of Tables

Table 1: Total area of Land Cover Classes in the region of Friuli Venezia Giulia based on Corine Land Cover (Copernicus Program 2018) ...... 12 Table 2: Environmental factors influencing the distribution of the golden jackal, which were used to generate the habitat suitability model...... 21 Table 3: Total number of samples from the snow & mud tracking survey in February and March 2019 for each of the species of interest. Total number (TN) of signs of presence and the highest (High) and lowest (Low) elevation, at which signs of presence were found, as well as average altitude (Mean) and standard deviation (SD) are shown...... 28 Table 4: ANOVA for testing for a significant difference between the golden jackal, red fox and grey wolf in terms of altitudinal distribution from the snow & mud tracking data...... 29 Table 5: Tukey post-hoc test for testing significant differences in altitude between the golden jackal, the grey wolf and the red fox from the snow & mud tracking data...... 29 Table 6: Species chosen as target group species for establishing the target group background for the habitat suitability model for the golden jackal. The data was collected during the snow & mud tracking survey in February and March 2019 using the same method as for the grey wolf, the golden jackal and the red fox...... 30 Table 7: Camera traps deployed in the field with the respective deployment time in days during the timeframe of the snow & mud tracking survey. MA = macro area, GJ = golden jackal, RF = red fox, GW = grey wolf. If a camera trap had to be removed early from the field the reason is stated in the notes...... 32 Table 8: Statistical output on training and testing gain and AUC of the habitat suitability model for the golden jackal...... 40 Table 9: Variable contribution of each environmental factor to the habitat suitability model for the golden jackal in order of magnitude...... 42 Table 10: Training gain and testing gain as well as AUC of the jackknife test of the habitat suitability model for the golden jackal with each environmental factor being left out of the overall model (…Without Variable) and each variable being the only variable in order of magnitude of the training gain without variable...... 44 Table 11: Most important categories within the Corine Biotopes Habitats environmental factor derived from the response curve of the overall habitat suitability model for the golden jackal. For the complete table see Annex Table 17...... 48 Table 12: Most important categories within the Corine Biotopes Habitat environmental factor derived from the response curve. The values of habitat suitability are derived from a model where this is the only environmental factor considered. For the complete table see Annex: Table 18...... 48 Table 13: Description of the first four CLC-classes and the corresponding habitat suitability derived from two response curves of the habitat suitability model. The overall habitat suitability is derived from the response curve of the overall model, the habitat suitability as only variable is derived from a model with only CLC as an environmental factor. For the complete table see Annex: Table 18...... 48 IX

Table 14: Description of the environmental factor forest types and the respective habitat suitability of the relevant categories. This data is from the response curve of the overall habitat suitability model and only the first five categories are shown. For the complete table see Annex: Table 19...... 49 Table 15: Description of the environmental factor forest types and the respective habitat suitability of the relevant categories. This data is from the response curve of the habitat suitability model with only forest types as an environmental factor and only the first five categories are shown. For the complete table see Annex: Table 20...... 49 Table 16: Habitat suitability for Natura 2000 areas from the response curve of the habitat suitability model for the golden jackal. Overall habitat suitability was calculated from the overall model, habitat suitability as only variable was calculated using only Natura 2000 areas as environmental factor...... 52 Table 17: Complete results for the habitat suitability model of the golden jackal for Corine Biotopes Habitats with the habitat suitability derived from the overall model...... 74 Table 18: Complete results for the habitat suitability model of the golden jackal for Corine Biotopes Habitats as only variable...... 76 Table 19: Full table of the single CLC-classes and the corresponding habitat suitability derived from two response curves of the habitat suitability model, ordered by magnitude of the Overall Habitat Suitability. The overall habitat suitability is derived from the response curve of the CLC environmental factor from the overall model, the habitat suitability as only variable is derived from a model with only CLC as an environmental factor...... 80 Table 20: Complete table of the response curve for forest types as environmental factor for the overall habitat suitability model for the golden jackal...... 81 Table 21: Complete table of response curve from the habitat suitability model for the golden jackal for the environmental factor forest types, when it is the only variable...... 87

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1 INTRODUCTION

1.1 SPECIES DISTRIBUTION MODELS (SDM) The spatial distribution of species is highly dynamic with a constant habitat shift of native species and neobiota, e.g. due to global change (Langham et al. 2015; Di Minin et al. 2016; Pyšková et al. 2016). Species conservation and management relies heavily on data on distribution and density of the species of interest (Franklin et al. 2019). Rare species, such as the grey wolf or the golden jackal, are hard to detect (Thompson 2004), therefore statistical models could help identify possible habitats (Franklin et al. 2019). Species Distribution Models (SDMs) have been used for several different purposes, including management and conservation of threatened species, risk management, prediction of the impact of climate change or landscape management (Guillera-Arroita et al. 2015).

Depending on the data available, researchers have different possibilities to create species distribution models. For presence-only data, maximum entropy modelling has become a popular and widely used method (Guillera-Arroita et al. 2015; Aubry et al. 2017; Eriksson and Dalerum 2018). Especially for species with a wide range, maximum entropy modelling can be a powerful way to analyze current or future distributions (Eriksson and Dalerum 2018). It is known to outperform conventional methods of modelling species distribution, such as generalized additive models (Elith et al. 2006; Baldwin 2009).

Maxent (Phillips et al. 2006) is an open access software where presence-only data and specific environmental factors are used to create a maximum entropy model over a defined landscape (Merow et al. 2013). It is required to provide presence-only data of the species of interest as well as environmental factors which are thought to influence the species’ spatial distribution. The presence-only data are points of latitude and longitude which can come from various sources, e.g. telemetry data or data from non-invasive survey methods. Additionally, points of background data are generated. This data, which can be chosen randomly across the spatial extent of the dataset, represents absence data, where it is not known if the species is present (Merow et al. 2013). Phillips et al. (2009) refer to the background data as “pseudoabsence” data.

1.2 CARNIVORE INTERACTION With species shifting their distribution and (re-)colonizing habitats throughout Europe, gathering knowledge about their interaction can facilitate not only conservation measures but also management decisions. On a European level, species are often found moving from the south-east to the north-west, colonizing new, previously unsuitable areas (Pyšková et al. 2016). Range expansion in Europe is currently happening for many carnivore species (Chapron et al. 2014). For example, populations of apex predators, e.g. the grey wolf (Canis lupus) or the brown bear (Ursus arctos), are recovering throughout the continent, be it through reintroduction or independent recovery (Merkle et al. 2009). 1

Apex predators are defined by Ritchie and Johnson (2009) as “species which occupy the top trophic position in a community”. Below the apex predators, meso-predators, which are likely smaller in size than the apex predator, play an important functional role in the ecosystem (Ritchie and Johnson 2009). -predators are defined by Krebs (2014) as predators, who are killed by more than one other, co-occurring and higher-ranking predator. Usually, the term apex predator is used, when one animal stands at the top of the food chain, whereas the term top-predator is used when more share that position. In this thesis, the term apex predator is used as a for top-predator. However, the top trophic position is shared between the grey wolf (Canis lupus) and the brown bear (Ursus arctos) in the study area.

The functional role of a predator in an ecosystem can be of great importance, both for humans and wildlife (Fleming et al. 2017). It can range from regulating prey or subordinate predator species to facilitating the spread of diseases, such as rabies or mange (Fleming et al. 2017). Additionally, predators were found to have regulating effects on diseases of their prey species (Packer et al. 2003). In ecology, the Gause principle of competitive exclusion states that if two species require identical environmental conditions in the same habitat only one can survive, while the other expires (Gause 1934). However, apex predators and meso-predators can exist in the same habitat if a high abundance of feeding and hiding places and a large enough territory are given (Spassov and Acosta-Pankov 2019).

In general, canids () can have a variety of interspecific relationships, such as mutualism (each species benefits from the interaction), kleptoparasitism (stealing food from other animals) or intraguild predation (Saggiomo et al. 2017). For example, grey wolves and golden jackals have been observed feeding from the same carcass in the Eastern Rhodopes and Spassov and Acosta-Pankov (2019) recorded answers from both species simultaneously during acoustic stimulation in Bulgaria.

Nevertheless, within a guild of predators, such as canids, there is often intense competition about resources (Newsome and Ripple 2015), which can result in intraguild predation (Holt and Huxel 2007). There are two types of intraguild predation: one where the prey is killed for sustenance of the predator, and one where it is killed because it is an ecological competitor (Gese et al. 1996; Palomares and Caro 1999; Helldin et al. 2006). If the second option is the case, usually the larger animal kills the smaller one, i.e. the apex predator kills the meso- predator (Sergio and Hiraldo 2008).

Meso-predator release is a phenomenon that occurs when the apex predator, e.g. the grey wolf, is taken out of the environment causing a population outbreak of native meso-predators, e.g. red foxes, or an invasion or range expansion of alien meso-predators, e.g. golden jackals (Baum and Worm 2009). Such release and increase of certain meso-predators can be a challenge to management in terms of species and habitat conservation (Atkins et al. 2019).

The functional role of a species in an ecosystem is defined by the environmental circumstances in which the respective species occurs (Ritchie and Johnson 2009). In recent years, the 2

European golden jackal (Canis aureus moreoticus) has received increasing attention by researchers due to its range expansion and long-distance dispersal in northern and western European countries (Arnold et al. 2012; Šálek et al. 2014; Trouwborst et al. 2015; Pyšková et al. 2016). Such rapid range expansions can be concerning for the human population, e.g. in terms of safety of livestock against predators or of diseases (Rutkowski et al. 2015). Therefore, it is important to study possible interaction of such expanding species with other (native) species and their surrounding environment. In the study area, the golden jackal can be classified as a meso-predator, with grey wolves being the apex predator and red fox being the basal predator. In general, the golden jackal is seen as an ecological equivalent to the coyote (Canis latrans) (Jhala and Moehlman 2004).

Species interaction and co-occurring phenomena, such as intraguild predation or meso- predator release, have been studied for many canid species. For example, Helldin et al. (2006) suggest that in Sweden the population of red foxes (Vulpes vulpes) has declined about 50 % due to intraguild predation by the reintroduced lynx (Lynx lynx). Krofel et al. (2017) studied the interaction of golden jackals and wolves in Bulgaria, which is the country with the most overlap in territories of the species. They found that re-colonizing grey wolves displace golden jackals in the majority of cases (Krofel et al. 2017).

When grey wolves were eradicated from vast areas in North America, the population numbers of coyotes (Canis latrans) increased (Peterson 1995). With the grey wolf missing from the ecosystem, the coyote could be described as new apex predator (Ritchie and Johnson 2009). However, when grey wolves were reintroduced in the Yellowstone National Park, they frequently encountered coyotes near the carcasses of prey (Merkle et al. 2009). About 7 % of those encounters ended in grey wolves killing coyotes (Merkle et al. 2009). Generally, the number of encounters diminished over time, which suggests either a learning curve of the coyotes or a decrease in their number (Merkle et al. 2009)

As a result to intraguild predation, the inferior species can respond with alternated use of space or alternated activity patterns (Palomares and Caro 1999). For example, coyotes avoid areas where dens of grey wolves are located (Miller et al. 2012). Such behavior of avoidance could also be confirmed for golden jackals and red foxes in a field experiment in Israel (Scheinin et al. 2006). When presented a tray of food, red foxes avoided it when a captured golden jackal was near it (Scheinin et al. 2006). Interestingly, they did not avoid the food tray when only scent markings of golden jackals had been placed in its vicinity (Scheinin et al. 2006). However, when the setup switched, the golden jackals did not show as much interest in the displayed food as the red foxes did, neither with a captured red fox nearby nor without one (Scheinin et al. 2006). Although there might be several reasons for the behavior of the golden jackals, it does not entirely support the hypothesis of Ritchie and Johnson (2009), where they propose that apex predators actively pursue meso-predators installing fear among them.

Laundré et al. (2001) introduced the term “landscape of fear” which essentially describes prey species, that neglect foraging opportunities in resource-rich but risky habitat due to predation 3

risk, thereby creating space for other species to thrive. This theory could also be applied to the relationship of apex- and meso-predator (Ritchie and Johnson 2009). The meso-predator release caused by absence of the apex predator, is likely what caused the success of the golden jackal in colonizing new territory in recent years (Krofel et al. 2017). The top-down effects that grey wolves have on golden jackals in a human-dominated landscape were shown by Krofel et al. (2017). However, Newsome et al. (2017) suggest, that apex predators can only completely suppress meso-predators, when the apex predator occurs in high densities over large areas.

1.3 RESEARCH QUESTION In Europe, there are currently an estimated 97,000-117,000 golden jackals (Ranc et al. 2018b) and approximately 17,000 grey wolves (Boitani 2018), with an increasing trend in numbers in both species since the 1990’s (Krofel et al. 2017). Their territories seldom overlap, since grey wolves rather occupy territories in the mountains, while golden jackals prefer to stay in the lowlands (Krofel et al. 2017). Therefore, the Eastern Alps represent a very interesting area, since there are reproductive grey wolves in the lowlands and golden jackals, which occur in higher elevations (Krofel et al. 2017).

The goal of this study is to evaluate suitable habitat for the golden jackal in Friuli Venezia Giulia and to compare it with information on habitat use by the grey wolf and the red fox from literature reviews. Furthermore, I want to investigate if the target species can be separated by altitude in the study area. Additionally, possible ways of interaction of golden jackals with grey wolves and red foxes will be analyzed.

1.4 DESCRIPTION OF THE TARGET SPECIES

1.4.1 The Golden Jackal Canis aureus L., 1758

1.4.1.1 Current Distribution The golden jackal (Canis aureus) is a generalist carnivore, which is widespread across Africa, Asia, the Arabian Peninsula and South-Eastern Europe (Lapini et al. 2011; Hoffmann et al. 2018). After the African golden wolf (Canis anthus) has been found to be genetically distinct from the golden jackal and therefore, the European golden jackal (Canis aureus moreoticus I. Geoffroy Saint-Hilaire, 1835) is considered the largest subspecies of its taxon (Knispel Rueness et al. 2011; Lapini et al. 2011; Koepfli et al. 2015). Hereafter, the European golden jackal is simply referred to as golden jackal.

Until the mid of the 20th century, the golden jackal was found mainly in South-Eastern Europe, i.e. the Balkans, Anatolia and Caucasus region with a relatively widespread distribution (Spassov and Acosta-Pankov 2019). In the mid 1950’s and 1960’s a population minimum was recorded, probably triggered through habitat loss and poisoned bait (Spassov 1989; Giannatos et al. 2005), leaving only small core populations in the Balkans, e.g. Bulgaria (Spassov and Acosta-Pankov 2019). Since then, the species has expanded north- and westward, reaching

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European countries as far as Switzerland, Denmark, the Netherlands (Pyšková et al. 2016) France (European Wilderness Society 2017) or Finland (Teivainen 2019).

Long-range expansion, as described by Rutkowski et al. (2015), is a key element of the successful spread of the species. Numerous studies address this expansion of the golden jackal (Arnold et al. 2012; Šálek et al. 2014; Galov et al. 2015; Lanszki et al. 2015; Rutkowski et al. 2015; Trouwborst et al. 2015; Ćirović et al. 2016; Krofel et al. 2017; Newsome et al. 2017; Markov et al. 2018). There are several hypotheses regarding the reason for the success of the golden jackal in its new territories. Climate change, land-use change and land abandonment have been identified as some of the driving factors (Giannatos 2004; Arnold et al. 2012; Šálek et al. 2014; Pyšková et al. 2016). Additionally, the fact that predators of golden jackals, i.e. grey wolves, are absent from large parts of Europe has had a positive impact on the species’ success (Arnold et al. 2012).

1.4.1.2 Morphology and Ecology The golden jackal is a European generalist meso-predator, who hunts but also scavenges on prey of other animals or human food waste (Fleming et al. 2017). Jhala and Moehlman (2004) describe the golden jackal as ecological equivalent to the coyote. As mentioned before, the golden jackal is the largest subspecies of its taxon (Lapini et al. 2011) with an average length of 120-125 cm, 10-13 kg of body weight (Giannatos 2004) and relatively short legs (Spassov 1989). Its fur is mainly of a golden-brown and silverish color, with individual markings in the head and throat area (Giannatos 2004).

Golden jackals are mostly found in territorial groups, composed of the reproductive pair and their cubs (Demeter and Spassov 1993). Often, young females stay with the family group for a longer period and become so called helpers, while the males tend to wander off sooner (Demeter and Spassov 1993). Their den is usually situated in a protected area, e.g. under a tree or in sandy soil (Ivory 1999).

Since golden jackals have relatively short legs pursuing and catching prey is challenging (Spassov 1989). It also makes them not well suited for long-term survival in areas with deep snow cover (Spassov 1989). Therefore, harsh winters with low temperatures and deep snow cover as well as rough terrain and large forest massifs represent climatically and topographically limiting factors for the species’ distribution (Spassov and Acosta-Pankov 2019). In April 2019, the Golden Jackal Informal Study Group (GOJAGE) published a new altitude record for the golden jackal: one individual was photographed at 2350 m near Livigno, Sondrio (ITA). However, this was probably a dispersing individual and not a resident golden jackal.

Natural and food based factors, which relate to the presence of the golden jackal have been studied by several authors (Spassov 1989; Rotem et al. 2011; Boskovic et al. 2013; Lanszki et al. 2015; Hayward et al. 2017; Spassov and Acosta-Pankov 2019). Hayward et al. (2017) state, that golden jackals are limited in prey selection by a bottom-up effect concerning the body- mass of the prey.

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The contents of the diet of golden jackals can alter with human-influenced events in the season, such as hunting or slaughtering season (Fleming et al. 2017). It has been found, that large game species were dominant in stomach content during the hunting season (Lanszki et al. 2015), whereas during the slaughtering season, the diet composition of golden jackals in Croatia switched to mainly livestock (Boskovic et al. 2013). Without human impact, the diet of the golden jackal largely consists of lagomorphs, birds, rodents or small mammals, e.g. voles (Cricetidae), but also of piglets of wild boar (Sus scrofa) (Lanszki et al. 2009; Lanszki et al. 2015). Specifically, a preference for brown hare (Lepus europaeus) could be detected by Hayward et al. (2017), whereas rodents and small mammals were consumed according to their availability. Furthermore, plant material such as berries or fruit can be an important component of the diet of golden jackals (Penezic and Ćirović 2015). In contrast to human-dominated landscapes there is no seasonality in the diet of golden jackals in wild, more remote areas (Lanszki and Heltai 2002).

The golden jackal can be found in various habitats, e.g. semi-deserts, grasslands, forests, agricultural land and even in semi-urban habitats or dump sites (Rotem et al. 2011; Šálek et al. 2014; Koepfli et al. 2015; Trouwborst et al. 2015; Ranc et al. 2018b, 2018a). In Greece, golden jackals prefer two distinct types of habitat: wetlands and riparian areas, where the highest golden jackal density has been detected, and the Mediterranean maquis shrubland (Giannatos et al. 2005).

Competition about resources could likely arise between golden jackals and red foxes, since they have a large dietary niche overlap (Lanszki et al. 2006), whereas grey wolves and golden jackals have distinctly different preferences in terms of diet (Krofel et al. 2017). Giannatos et al. (2005) describe the competition for resources with the grey wolf, where the grey wolf is the dominating species, as one of the possible reasons for the decline of the golden jackal population in Greece. Therefore, the presence of the grey wolf could be a limiting habitat factor in golden jackal distribution (Giannatos et al. 2005; Spassov and Acosta-Pankov 2019). Wolf presence, or rather the lack thereof, could be one of the drivers for the recent explosion in jackal populations in Europe (Krofel et al. 2017; Newsome et al. 2017).

Human activity can be another big impact on the species selection of habitat (Spassov and Acosta-Pankov 2019). Especially the destruction of habitat, e.g. destruction of structured landscapes through intensification of agriculture, and direct persecution of golden jackals has had strong influence on the species distribution (Spassov and Acosta-Pankov 2019). However, human presence is not necessarily only negative for the jackal: Lapini et al. (2011) state, that in Northern jackals have been found near relatively large human settlements, e.g. Udine or Trieste. In fact, agricultural practices with a certain intensity can be a positive predictor for the presence of golden jackals (Šálek et al. 2014). Intensity is the key word – as for many other species, structured landscapes with shrub-herbaceous vegetation and heterogeneous agriculture has a positive effect on the probability of presence of the golden jackal (Šálek et al. 2014). However, habitat selection can differ according to its availability. Šálek et al. (2014) showed, that in Serbia, where 48% of overall area are intensively used arable lands, the golden

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jackals selected extensively used shrublands, whereas in Croatia, where most area is used extensively, they selected for intensively used habitats where the edge effect was largest. Furthermore, the proximity to human settlements could provide additional food sources for scavenging, e.g. slaughtering waste (Spassov and Acosta-Pankov 2019).

Golden Jackals have a better image than the grey wolf in terms of livestock depredation. In Greece, kills of small hooved livestock by golden jackals (mostly by lone individuals) seldom occurs, and only in areas with low population densities (Giannatos et al. 2005). In territories with large densities of golden jackals, no livestock depredation has been recorded (Giannatos et al. 2005).

1.4.1.3 Golden Jackals in Friuli Venezia Giulia The first reports of golden jackals probably date back to the 1950’s, where several wolfpacks, which were likely misidentified golden jackals, were reportedly seen in the region (Lapini et al. 2009). About 30 years later, in the 1980’s, the golden jackals colonized the Karst around Gorizia and Trieste and the Julian Prealps, spreading into the low-land areas of Friuli Venezia Giulia (Lapini et al. 2009). In 2014, three to eight reproductive groups of golden jackals with 15-40 individuals were suspected to live in the north-east of the country (Lapini et al. 2011). To date around 20-25 groups of golden jackals were found to live in the region (Filacorda, S. personal communication). Fabbri et al. (2014) found that genetically the population in Northern Italy is a mixture of Dalmatian and Slavonian origin. The first evidence of reproduction near Udine is from in 1985, where two young jackals were observed (Lapini and Perco 1988; Lapini et al. 2011). Lapini et al. (2011) describe that golden jackals in Italy prefer habitat with mesophilic forests up to medium altitude, such as Fagetum, Aceri-Tilietum, Abieti- Fagetum, Orno-Pinetum nigrae, Pinetum and Salicetum. They also describe a clear preference for humid habitats, such as riparian woods (Lapini et al. 2011).

1.4.1.4 Legal Implications & Protection Golden jackals are protected directly and indirectly by various international laws and legal instruments (Trouwborst et al. 2015). On a European level, the first agreement to protect the golden jackal was within the 1979 Convention on the Conservation of European Wildlife and Natural Habitats (Bern Convention). Thereafter, in 1992 two legislations passed: The Convention on Biological Diversity (CBD) and the EU Directive 92/43 on the Conservation of Natural Habitats and of Wild Fauna and Flora (Habitats Directive) (Trouwborst et al. 2015). However, the species is neither listed under the 1973 Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) nor the 1979 Convention on the Conservation of Migratory Species of Wild Animals (CMS) (Trouwborst et al. 2015).

In some of the EU member states, e.g. Austria, Slovenia or Bulgaria, the golden jackal can be hunted, at least in parts of the countries (Trouwborst et al. 2015). In other European countries, it is fully protected, e.g. in Switzerland, or (Trouwborst et al. 2015). In Italy, the golden jackal has been fully protected since 1992 (Lapini 2003) and is considered as Least Concern (LC) in the IUCN Red List (Ranc et al. 2018b). In Annex V and VI of the EU Habitats

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Directive, the golden jackal is listed as a Community Interest Species (“Habitats Directive” 92/43/EEC), same as the European polecat (Mustela putorius), the chamois (Rupicapra rupicapra) and the pine marten (Martes martes) (Hatlauf et al. 2016a). Thus, an obligation to monitor the conservation status of all habitats (Annex I) and species (Annex II, IV & V) arises from Article 11 of the Habitats Directive (Hatlauf et al. 2016a). Every six years, a monitoring report has to be presented to the Commission (Article 17). Decisions for species management measures can only be assessed after such a report is presented (Hatlauf et al. 2016a).

Generally speaking, the arrival of a new species, such as the golden jackal, is relevant for policy makers and species conservation and management (Trouwborst et al. 2015). Notably, when the EU Habitats Directive was created in 1992, the only golden jackal population within the EU borders was a small, fragmented population in Greece (Trouwborst et al. 2015). However, the management of the golden jackal will, on the one hand with the addition of EU member states and on the other hand with the major range-expansion of the species, become of interest to policy makers (Trouwborst et al. 2015).

1.4.2 The Grey Wolf Canis lupus L., 1758

1.4.2.1 Current Distribution In Europe population numbers of the grey wolf have significantly decreased in many countries after the mid of the 19th century (Jedrzejewska et al. 1996; Krofel et al. 2017). During the first half of the 20th century, the population trend of grey wolves decreased further due to habitat loss, less prey availability and advanced hunting techniques (Jedrzejewska et al. 1996; Adamič et al. 1998). After World War II had ended, the European populations of the grey wolf experienced their all-time low, mainly caused by intense hunting, e.g. with poisoned bait (Chapron et al. 2014). Only remnant populations in isolated areas, e.g. remote mountains in Bulgaria, survived the human persecution (Krofel et al. 2017). Since then, grey wolves have started to recolonize their old habitat (Chapron et al. 2014). Now, grey wolves are the second most abundant large carnivore (after the brown bear) in Europe (Chapron et al. 2014), with approximately 17,000 individuals (Boitani 2018). On the one hand, this has had positive effects, namely the restoration of the trophic cascades within the ecosystem (Ripple et al. 2014). On the other hand, it has fueled discussions with farmers and hunters, who fear for their livestock and game species (Linnell and Boitani 2012). The populations of grey wolves in the alps have been growing, but it appears to do so rather slowly (Galaverni et al. 2016).

1.4.2.2 Morphology and Ecology The grey wolf is the largest of the European Canid species with 70-80 cm height, 100-145 cm body length and a 50 cm tail (Olsen 2017). It has a greyish fur, which can be darker along the spine and black at the shoulders or the tail (Olsen 2017). Generally, its color is lighter in winter (Olsen 2017).

Grey wolves live in packs, usually composed of the reproducing male and female and their cubs (Olsen 2017). However, they can form larger packs, especially in winter (Olsen 2017). They are territorial and therefore mark with urine or scats (Olsen 2017). Each pack has several 8

dens, e.g. a hole in sandy soil or underneath rocks, but when they have cubs they only use one (Olsen 2017).

Like other large carnivores, grey wolves live in big territories with a low density of individuals (Gittleman et al. 2001). Compared to all other large carnivore species of Europe, the grey wolf seems to have the best ability to adapt to a human-dominated landscape (Chapron et al. 2014). In comparison to the golden jackal or the red fox, grey wolves differ considerably in terms of diet (Krofel et al. 2017).

Fleming et al. (2017) classify the grey wolf as hyper-carnivore, whose diet consists of >70 % of meat, e.g. of ungulates, large vertebrates and carrion. The diet of the grey wolf in terms of preferred prey varies greatly across the European continent. In general, they prefer wild ungulates, e.g. red deer (Cervus elaphus), roe deer (Capreolus capreolus), wild boar (Sus scrofa) or in northern countries even moose (Ales ales) (Jedrzejewski et al. 2000).

Grey wolves choose their prey according to species abundance (Meriggi et al. 2011). In the southern Apennines, where ungulate species are less abundant, they depredate more livestock than in the Alps (Meriggi et al. 2011). Meriggi et al. (2011) therefore state that the preferred prey are wild ungulates and livestock seems to be more of an alternative prey for the grey wolf. However, it remains unclear if the presence of the grey wolf can have regulatory effects on the abundance of its prey species (Ripple et al. 2014).

Grey wolves have a very broad spectrum when it comes to habitat selection (Boitani 2018). In general, large forests with a high abundance of prey are considered as favorable habitat for the grey wolf (Boitani 2018). However, as stated before, grey wolves are very adaptable and therefore can live in many different landscapes, including shrubland, open grasslands and even agriculturally cultivated areas (Boitani 2018).

1.4.2.3 The Grey Wolf in Friuli Venezia Giulia In 1869 the last grey wolf of Friuli Venezia Giulia was killed near the city of Pordenone (Vendramin et al. 2018). It then returned to the region only recently; in 2010 a solitary individual was captured by a camera trap near the Slovenian border in the Karst (Vendramin et al. 2018). Galaverni et al. (2016) used data from 2009-2013 for their study on wolf distribution in Italy, and only reported of transient, non-reproductive wolves in Friuli Venezia Giulia. In fact, the first contemporary evidence of reproduction comes only from 2018 from the area of Magredi near Pordenone (Pusiol 2018). In 2019, first evidence of a pair in the area of Cansiglio, on the border to the Veneto region could be found (unpublished data, this study).

1.4.2.4 Legal Implications & Protection In a human-dominated landscape like Europe, protecting a species like the grey wolf is not easy (Linnell and Boitani 2012). Production of goods, e.g. crops and timber, or recreational use of the landscape often stand in opposition to the species conservation goals (Linnell and Boitani 2012). Management of large carnivores, in contrast to management of meso-

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carnivores, is a delicate process, which has to take into account not only the species’ needs, but also the social, economic or cultural needs of the people (Linnell and Boitani 2012).

The grey wolf has been protected by the 1979 Bern Convention in Appendix II, and it is mentioned in the EU Habitats Directive in Annex II and Annex IV, along with the brown bear (Ursus arctos) or the Eurasian lynx (Lynx lynx) (Linnell and Boitani 2012). Especially, when the eastern European countries entered the EU, the Habitats Directive gained more and more importance (Linnell and Boitani 2012). The potential of conflicts between grey wolves and humans is very high, especially in terms of livestock depredation or “sharing” the same prey with hunters (Linnell and Boitani 2012). Apart from those “economical” conflicts, many humans simply fear the presence of the grey wolf (Linnell and Boitani 2012). Legal culling, e.g. by setting quotas for hunting a specified number of individuals, can be established in countries, where the conservation status is favorable and where the populations viability is not threatened (Trouwborst and Fleurke 2019). For example, the national wolf plan of France imposes that 10-12 % of the French wolf population may be killed each year (Trouwborst and Fleurke 2019).

1.4.3 The Red Fox Vulpes vulpes L., 1758

1.4.3.1 Current Distribution The red fox is one out of ten species within the Vulpes and it is the most common and best-known of all of them (Lariviere and Pasitschniak-Arts 1996). It is also the carnivore with the widest worldwide distribution (Voigt 1987). Red foxes are distributed across most of the northern hemisphere, with the exception of territories in the far north, such as Siberia (Voigt 1987). They have been introduced by humans to and North-America (Voigt 1987). The red fox has a very high altitudinal range, from 0 to 4500 m (Hoffmann and Sillero-Zubiri 2016).

Habitat loss and persecution by humans are the main threats red foxes face worldwide (Hoffmann and Sillero-Zubiri 2016). Because of their versatility and wide range of possible habitats and diets, they are very likely to adapt to changing conditions (Hoffmann and Sillero- Zubiri 2016). However, much of their current distribution range is due to human-assisted dispersal, e.g. for recreational or economic purposes (Saunders et al. 2010; Fleming et al. 2017). On the IUCN Red List, it is listed as Least Concern (LC).

In Friuli Venezia Giulia the red fox is a highly abundant animal and it is a hunted species throughout the whole country of Italy. The census for the hunting statistics in 2019 revealed, that 5,258 red foxes are abundant in the region (RAFVG 2019). The census was held in all parts of Friuli Venezia Giulia, with the exception of the Parco Naturale Regionale delle Prealpi Giulie and the Riserva Naturale Val Alba.

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1.4.3.2 Morphology and Ecology The red fox is relatively small with a long snout, pointy ears and a long and bushy tail with a white tip (Lariviere and Pasitschniak-Arts 1996). It is of red to orange-yellowish color with the distinct characteristic, that the backside of its ears and part of its legs are dark (Lariviere and Pasitschniak-Arts 1996; Olsen 2017). Red foxes can have a weight 8 – 15 kg and have a body length of around 70 cm (Olsen 2017). Females are generally smaller than males (Olsen 2017).

Like the golden jackal or the grey wolf, the red fox is a territorial species (Olsen 2017). They mark their territory with urine and scats (Olsen 2017). The red fox is a solitary scavenger and opportunistic predator (Saunders et al. 2010), but in high densities it is known to form groups of up to ten individuals, because of limited possibility of establishing their own territory (Iossa et al. 2009). The red fox often has its den on south facing slopes with sandy soils, but it can also use natural structures like holes underneath rocks (Olsen 2017). Dorning and Harris (2019) showed, that territorial red foxes are rather lenient towards individuals of the same species when they enter “foreign” territory.

The red fox can live in various habitats, such as semi-arid deserts, farmland, boreal forests or tundra (Lariviere and Pasitschniak-Arts 1996). They generally prefer heterogenous habitats with prey availability being the determining factor of habitat use (Phillips and Catling 1991).

The diet of the red fox usually consists of small mammals, such as squirrels (Sciuridae), rodents, lagomorphs or ground nesting birds (Jedrzejewski and Jedrzejewska 1992; Helldin and Danielsson 2007). Additionally, they eat berries, other vegetation, carrion and regularly feed on kills of other predators (Helldin and Danielsson 2007). Larger predators, such as the coyote or the grey wolf, usually show aggressive behavior against the red fox (Gese et al. 1996). However, coyotes can tolerate red foxes, when food is available in large quantities, given that the coyote has fed before the red fox (Gese et al. 1996).

In Italy, two larger canids, the grey wolf and the golden jackal, are found to be sympatric with the red fox. In fact, the presence of the grey wolf could prove to be beneficial for red fox populations. Newsome and Ripple (2015) state that red foxes outnumbered coyotes in north American territories, when the grey wolf was present. In fact, Levi and Wilmers (2012) showed, that as a result of the suppression of the coyote by the wolf, the red fox experienced a release effect, at least on a small spatial scale.

The red fox can have a great impact on its surrounding environment, e.g. by causing population decline in small species or overall biodiversity loss in areas where it is not native (Saunders et al. 2010). Due to their flexibility in habitat and food choice, the red fox can survive in almost every environment (Fleming et al. 2017). Especially rural, peri-urban or urban landscapes can have the capacity to carry large population densities of red fox (Bino et al. 2010).

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2 MATERIAL AND METHODS

2.1 STUDY AREA

2.1.1 Geography The autonomous region of Friuli Venezia Giulia is the most north-eastern region of the state of Italy. It shares a border with Austria in the north and Slovenia in the east. In the west it borders to the region of Veneto. The regional capital is Trieste, which is situated the southernmost tip of the region. It is divided into the low-lands in the south towards the Mediterranean Sea and the mountainous areas in the north. The mountains are partitioned into the Carnian Prealps and the Julian Prealps, reaching altitudes of over 2500 m. The region is divided into four provinces: Pordenone in the west, Udine in the north and east and Gorizia in the south-east. The fourth province is Trieste, which makes up the small strip of land in the south-east. The total area of the region is 7706.4 km² and approximately 1.2 million people live there. The population density is 155 inhabitants per km².

Forests (38.4 %) are the most abundant class of Corine Land Cover, followed by arable lands (23.8 %) and heterogenous agricultural areas (12.4%) (Copernicus Program 2018) (Table 1). The character of the region is rural, with major settlements concentrating in the plains in the southern part. The most important rivers in Friuli Venezia Giulia are the Tagliamento and the Isonzo (Soča) river. In order to partition the region in several smaller research areas, I modified the macro areas Comazzi et al. (2016) defined in the region by adding more macro areas (Figure 1).

Table 1: Total area of Land Cover Classes in the region of Friuli Venezia Giulia based on Corine Land Cover (Copernicus Program 2018)

Area [km²] % Forest 2957.5 38.4 Arable land 1834.1 23.8 Heterogeneous agricultural areas 958.8 12.4 Shrub and/or herbaceous vegetation associations 611.7 7.9 Open spaces with little or no vegetation 474.2 6.2 Urban fabric 466.8 6.1 Permanent crops 147.0 1.9 Industrial, commercial and transport units 130.8 1.7 Pastures 53.8 0.7 Inland waters 22.0 0.3 Marine waters 16.6 0.2 Artificial, non-agricultural vegetated areas 12.4 0.2 Coastal wetlands 12.2 0.2 Mine, dump and construction sites 7.9 0.1 Inland wetlands 0.6 0.0

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Figure 1: Map of the macro areas in Friuli Venezia Giulia chosen for the snow & mud tracking survey and the acoustic stimulation survey adapted from Comazzi et al. (2016). Each macro area is labelled by a code: MA1 (Cansiglio), MA2 (Magredi), MA3 (Carnia/Upper Tagliamento), MA4 (Middle Tagliamento), MA5 (Lower Tagliamento), MA6 (Val Aupa/Glazzat), MA7 (Julian Pre-Alps/Natisone), MA8 (Torre), MA9 (Goritian Karst) MA10 (Trieste Karst).

2.1.2 MA1: Cansiglio The plateau of Cansiglio is situated in the western part of Friuli Venezia Giulia, that has its border to the region of Veneto. It is a karstic plateau of the Carnian Prealps (Prealpi Carniche). The maximum elevation is 2200 m, with two plateaus at about 1000 m: Cansiglio and Piancavallo. Large forests of beech (Fagus sylvatica) and open grasslands can be found there. The area is widely known for winter tourism, i.e. skiing, but there is also some pasturing activity and agro-tourism in the summer months. The forests of Cansiglio are protected under Natura 2000.

2.1.3 MA2: Magredi This area is located north of Pordenone between the river Meduna and the river Cellina. It is a flat, low-land area, characterized by large deposits of fluvial pebbles and sediment. It gets its name from the generally meagre habitat and soil conditions (Figure 2). The area along the two rivers is designated as Natura 2000 area. It is used for pasturing of sheep or goats and the

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cultivation of crops, such as wine. The main vegetation is composed by small shrub- and grass species with occasional assemblies of trees.

Figure 2: Examples of a typical environment of MA2: Magredi (left) in the lowlands near Pordenone and MA5: lower Tagliamento (right).

2.1.4 MA3: Carnia/Upper Tagliamento The area is located east of (323 m of altitude) along the Tagliamento river and its tributaries. It is mainly characterized by the dynamics of the Tagliamento river, one of the most natural rivers of Europe, with riparian vegetation along the banks, mainly of coniferous trees and shrub-herbaceous vegetation. The river has a rather natural flow dynamic with changing channels and shifting banks, which can lead to sudden changes of habitat conditions close to the river. On the north side of the river the local population practices agriculture and pasturing of cattle and other animals, whereas on the south side of the river mainly coniferous forest and steeper slopes can be found.

2.1.5 MA4: Middle Tagliamento This area is located south of Tolmezzo along the Tagliamento river. The river dynamics are similar to MA3; however, the channel widens even more as the river enters the plains. On the western side of the river, there are mainly hills and forest while on the eastern side there are larger settlements, industry and extensive agriculture.

2.1.6 MA5: Lower Tagliamento Further to the south along the course of the Tagliamento river, agriculture and pasturing activities increase. Only relatively small strips of riparian vegetation along the river banks remain and human impact is more pronounced in this area (Figure 2). The human settlements 14

are more scattered and almost all of the land surrounding the river is used for agricultural purposes. The fields are however mostly surrounded by small strips of forest or shrubland, giving the landscape a high amount of structure.

2.1.7 MA6: Val Aupa/Glazzat This area is relatively secluded from human impact without major settlements. Many abandoned houses or even entire villages can be found there and there is a high degree of scrub encroachment. There are a few agro-tourism facilities and pasturing on the meadows. The main characteristic however are relatively steep slopes with forest cover. There are frequent events with high amount of precipitation in the region, especially in the fall months.

2.1.8 MA7: Julian Pre-Alps/Natisone Situated on the border to Slovenia, part of the area is located in the “Parco Naturale Regionale delle Prealpi Giulie” (Natural Park of the Julian Prealps). It is mostly covered by forest with agricultural activities being mainly cultivation of wine. The topography is characterized by steep slopes.

2.1.9 MA8: Torre The area lies south of the city of Udine along the Torre river. Along the river small strips of riparian vegetation mainly of small trees and dense shrubland can be found. However, the extent of the shrubland is rather small and it often borders to agricultural fields. In the south of the area, the agriculture intensifies, leaving less structures for animals to hide in.

2.1.10 MA9: Goritian Karst & MA10: Trieste Karst The Italian Karst is characterized by its limestone bedrock, which in the course of time has been eroded by water leaving large caves and holes in the rock. It is a plateau with low altitudes and due to its geographical situation near the coast winters are mild with little snow cover. Due to the dissolution of the bedrock by water, structures like dolines and furrows (“karrenfeld”) are common. It is an area, which is actively used by farmers for pasturing mainly sheep and goats. The vegetation is a dense shrubland with dominating Rubus sp. and Rhus cotinus. Dominating tree species are Quercus pubescens, hornbeam (Carpinus betulus) and ash-tree (Fraxinus sp.).

2.2 FIELD SURVEYS Species occurrence data were collected mainly through snow & mud tracking and acoustic stimulation surveys. Additionally, we deployed camera traps in selected areas. All macro areas (Figure 1), except MA9 and MA10 have been sampled in the snow & mud tracking survey 2019. Acoustic stimulation has been performed in all macro areas, except for MA1.

2.2.1 Snow & Mud Tracking Snow & mud tracking is a form of non-invasive monitoring, which has gained increasing importance in wildlife research (Alexander et al. 2005). This method has been used specifically for estimating the presence and distribution of carnivores (Ciucci et al. 2003; Alexander et al.

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2005; Squires et al. 2012). We held a snow & mud tracking survey in the winter of 2019 from late January to April in the whole region of Friuli Venezia Giulia (Figure 3).

Snow tracking largely depends on the snow cover and whenever possible it was conducted after fresh snow had fallen. Mud tracking was conducted mostly along the sandy banks of rivers. We did linear transects of approximately 1-1.5 km by foot in the opportunistically selected sites (Figure 3) following roads, forest roads or hiking paths. We collected data on all large and medium sized carnivores and herbivores and attempted to record each individual animal only one time (Squires et al. 2012). For example, if an individual followed the transect, we only took one record of it. Signs of presence found during the snow & mud tracking survey can be footprints, scats, hair or other genetic material, e.g. saliva or tissue. For this study, only data on footprints and scats are available.

We documented all tracks with an app called Cybertracker (CyberTracker Conservation 2013), with which we recorded the coordinates for each animal track. When we found footprints of a golden jackal or a grey wolf, we took additional measurements of footprint size and stride and added them to the protocol (Figure 4). When we found scats of golden jackals or grey wolves, we took them back to the lab for further analysis. Scats have been determined by a trained professional but not by DNA analysis.

In addition to the analysis of the distribution of the target species, this data is used to assess differences in habitat use in terms of altitude. A one-way ANOVA and a Tukey post-hoc test were performed in R to detect significant differences in terms of altitude of the three target species.

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Figure 3: Snow & mud tracking locations in the region of Friuli Venezia Giulia. The blue asterisks represent snow & mud tracking locations of the season 2019. White points locate the major cities of Friuli-Venezia-Giulia for better orientation, blue lines represent the major rivers of the region.

Figure 4: Examples for the signs of presence documented in the snow & mud tracking survey. Left a footprint of a golden jackal can be seen, right the scat of a grey wolf is depicted.

2.2.2 Acoustic Stimulation Acoustic stimulation (sometimes referred to as bioacoustic stimulation (BAS) in literature) has become a very popular method to assess the distribution and density of golden jackals in Europe (Giannatos et al. 2005; Lapini et al. 2009; Banea et al. 2012; Mihelič and Krofel 2012; Comazzi et al. 2016; Acosta-Pankov et al. 2018). All species of the genus Canis are highly 17

dependent on their social group and share many similar behavioral and ecological traits (Bekoff et al. 1981). Howling is a widespread technique to communicate over a long range, to signal both group cohesion and territorial boundaries (Kershenbaum et al. 2016). In addition to the well-known howling, all species have a variety of short range communications, such as barks, yips or growls (Cohen and Fox 1976).

Grey wolves can identify individuals from their group by their howl and if one individual is removed from the group, the howling pattern of the group can change (Mazzini et al. 2013). For some canid species, e.g. the (Canis lupus dingo), differences in howling can also be found on an individual level (Déaux and Clarke 2013). On a species level, Kershenbaum et al. (2016) showed, that larger species, like the grey wolf, tend to use less frequency modulation than smaller species, like the golden jackal (Figure 5). The smaller species in turn tend to end their howls with a sharp drop in frequency (Kershenbaum et al. 2016).

Figure 5: Frequency modulation in canid howls with the example of the golden jackal and the European grey wolf (modified from Kershenbaum et al. 2016).

Howling as a form of communication between group members could also be related to hunting techniques of the pack (Muntz and Patterson 2004). Using a play-back method for stimulating howling in golden jackals has proved to be very successful (Giannatos et al. 2005).

Generally, territorial groups of golden jackals are more likely to answer than lone individuals (Giannatos et al. 2005). Additionally, no answer to a stimulus must not be translated to absence of the species (Giannatos et al. 2005). Therefore, multiple stimuli (i.e. emissions) at one location after a short period of time should be done, in order to generate answers from shyer animals (Giannatos et al. 2005).

Several types of long-distance vocalizations of golden jackals were be identified: two long calls (warble and flat howl) and one short call (yip) (Kolar et al. 2005). Acosta-Pankov et al. (2018) showed, that the howling behavior of golden jackals was increased in the months when the cubs start emerging from the dens and new territories and family groups are formed.

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Generally, the howling behavior of golden jackals should signal territoriality, identify family group members and ascertain distribution (Acosta-Pankov et al. 2018).

From 2010-2019, acoustic stimulation surveys have been carried out in selected macro areas. The calling stations have been semi-opportunistically selected from a 3 x 3 km grid over the area of Friuli Venezia Giulia (Comazzi et al. 2016). Within one survey only one calling station per grid cell was visited, some grid cells were however sampled more than once. If the same calling station was visited more than one time, a minimum break of 30-45 days between the stimulations was kept, even though recent data suggests that overstimulation of golden jackals through acoustic surveys does not impact the results significantly (Fanin et al. 2018).

If possible, the calling stations have been positioned on an elevated location, in order to maximize the hearing distance. Additionally, we attempted to minimize background noise, e.g. from villages or road traffic by selecting suitable sites. Logistic effort was minimized by choosing locations for calling stations, which were accessible by car or by a short walk. The surveys were conducted during the night at least one hour after sunset and at least one hour before sunrise respectively (Giannatos et al. 2005; Šálek et al. 2014). At each calling station, the survey was held on two consecutive nights, if the weather conditions allowed it. Generally, the surveys were held on dry nights with little to no wind. The maximum hearing distance of humans was described to be 1.8 to 2 km (Giannatos et al. 2005). Depending on the terrain, this distance can become smaller and was estimated at approximately 1.5 km (Acosta-Pankov et al. 2018). The calling stations were located within a distance of around 3 km from each other, in order to prevent counting the same pack or individual twice.

A soundtrack of either a single individual or a group howl (depending on the season) was played for 30 s at 85-90 dB using a loud speaker (Comazzi et al. 2016). After that, at least two persons listened for answers for another 3 min. Each emission was repeated for 5 times. In case an answer of golden jackals was detected, the emissions were stopped and no further emission was done at the respective calling station. We added the direction and the duration of the response, as well as an estimation of the distance and number of individuals to the protocol. Each answer was counted as one territorial individual or group, similar to Šálek et al. (2014).

Overall, 359 calling stations were visited before this study, the majority of calling stations was visited more than once. In total 56 answers of golden jackals were detected, which I used for this study as well. This data has previously been used by Comazzi et al. (2016) and Confalonieri et al. (2012). Furthermore, I added new data, partly collected by myself in 2019, to the existing dataset. We visited 44 calling stations, each of them on two consecutive nights.

2.2.3 Camera Trapping Camera traps have been successfully used for monitoring various species, including the golden jackal (Pecorella and Lapini 2014; Pyšková et al. 2016). We opportunistically placed various models of camera traps at locations, where we had previously either heard golden jackals at acoustic stimulation surveys or where tracks of golden jackals or wolves had been found. This

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way we attempted to get visual confirmation of the presence of one or more of the species of interest. For the red fox no specific cameras were placed, it was however noted, when a red fox was present on one of the cameras. We attempted to direct the cameras at passages used by wildlife away from paths used by humans. At one camera trap, we placed a dead roe deer (Capreolus capreolus) and at two camera traps we placed fish bait, which was proven to attract golden jackals by Pecorella and Lapini (2014).

2.3 MAXENT-DATA

2.3.1 Presence Points For the analysis in Maxent, presence points of golden jackals collected in the snow & mud tracking survey in 2019 and presence points collected between 2010 and 2019 through acoustic stimulation surveys were used. Some presence points, which had wrong coordinates or were out of the bounds of the study area had to be excluded. This left a total of 234 presence points for the golden jackal. For using the data in Maxent, the coordinates were transformed into the same coordinate system used for the environmental data: ETRS_1989_LAEA.

2.3.2 Environmental Factors Based on literature research (Lapini et al. 2011; Hatlauf et al. 2016b; Ranc et al. 2017; Spassov and Acosta-Pankov 2019) I identified several environmental (e.g. topographic or climatic) factors (Table 2), which influence the distribution of the golden jackal and which will be explained in more detail in this chapter. I assumed that the environmental factors Corine Land Cover (CLC) and Corine Biotopes Habitats (Habitat FVG) offer enough information to distinguish between natural area and human settlement, therefore no data on roads, towns or other infrastructure were added.

The environmental factors were displayed in ArcMap and they were modified to all have the same spatial resolution and extent. Finally, all environmental factors were transformed to ASCII-files in order to be uploaded to Maxent. Since Maxent uses a machine learning approach, it is not necessary to reduce correlation in the environmental dataset (Elith et al. 2011; Merow et al. 2013).

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Table 2: Environmental factors influencing the distribution of the golden jackal, which were used to generate the habitat suitability model.

Details Original Resolution Original Data Type Elevation Altitude of each pixel cell 25 m x 25 m Raster Precipitation Average monthly precipitation 1 km x 1 km Raster and average annual precipitation Temperature Average monthly temperature 1 km x 1 km Raster and average annual temperature Corine Land Landcover types 100 m x 100 m Vector (Polygon) Cover Corine Biotopes Corine Biotopes Habitats 1:25000 Vector (Polygon) Habitats Forest Types Forest types according to 1:5000 Vector (Polygon) environmental conditions Rivers River network from the 1:50000 Vector (Line) Danube and Po catchment Snow Cover Average annual number of 250 m x 250 m Raster Duration days each pixel is covered by snow in the winters of 2002- 2014 Terrain Derived from DEM, shows the 25 m x 25 m Raster Ruggedness ruggedness of the terrain Index Natura 2000 All areas specified as Natura 1:5000 Vector (Polygon) 2000 areas

2.3.3 Digital Elevation Model (DEM) The digital elevation model (DEM) (EU-DEM v1.1 2016) with a resolution of 25 m and a vertical accuracy of ± 7 m from the European Environment Agency (EEA) Copernicus website was used. The tile used for this study is called EUDEM2_EUROPE_4 (Danube). The coordinate system is EPSG: 3035 (ETRS 89 LAEA) (Copernicus Program 2016b). The DEM was cut out along the border of Friuli Venezia Giulia, which was obtained from the official governmental webGIS (http://irdat.regione.fvg.it/WebGIS/).

All other data was modified to match the DEM in terms of resolution, coordinate system and extent in order to be used in Maxent.

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2.3.4 Precipitation and Temperature Data from the “worldclim” dataset (Fick and Hijmans 2017) in a spatial resolution of 30s (~1km²) was used. Both datasets (precipitation and temperature respectively) are composed of average precipitation data and average temperature data for each month respectively ranging from 1970-2000 (Fick and Hijmans 2017). With this data, I additionally created data on annual average precipitation and annual average temperature using the raster calculator in ArcMap.

2.3.5 Corine Land Cover (CLC) Corine Land Cover (CLC) 2018, Version 20 (Copernicus Program 2018) was used. The dataset was produced following the standard methodology in nomenclature. The minimum mapping unit (MMU) is 25 ha for status layers, minimum width for linear units is 100 m and the MMU for land cover changes is 5 ha. The spatial resolution is 100 m (Copernicus Program 2018).

2.3.6 Corine Biotopes Habitats The map of Corine Biotopes Habitats was generated in 2017 by the Friuli Venezia Giulia Autonomous Region - Environmental Impact Assessment Office. It provides in-depth information on plant landscapes and anthropogenic systems and includes secondary and punctual habitats. It was produced on a scale of 1:25000 (Environmental Assessment Service 2017).

2.3.7 Forest Typology The region of Friuli Venezia Giulia (Forest Management and Timber Production Service 2013) provides data on different forest types. The data were generated in 2013 by the RAFVG – DC production, trade, cooperation, agricultural and forest resources – Forest Management and Production Service. The last update was made in 2018. The different forest types were identified using data on plant composition, phytogeographical distribution, substrate of the soil, altitudinal range, thermal factor, the zone siting and anthropogenic influence. It was created on a scale of 1:5000 (Forest Management and Timber Production Service 2013).

2.3.8 River Network Data on the river network of the Po and Danube river basins from the EU-Hydro dataset (Copernicus Program 2016a) were used. The spatial resolution is 1:50000 created from a 20 m resolution image (Copernicus Program 2016a). For the analysis with Maxent, I created buffers around the line features of the rivers in ArcMap. I chose the size of the buffers according to the categories for distance to rivers established by Spassov and Acosta-Pankov (2019). Therefore, the buffers were separated into four categories, displaying the distance from a river: 1 km, > 1-2 km, > 2-3 km and > 3-10 km.

2.3.9 Snow Cover Duration (SCD) The data on snow cover duration (SCD) for each winter from 2002 – 2014 was calculated by Xie et al. (2017) using data provided by Notarnicola et al. (2013b, 2013a). The data was derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 250 m (Xie et al. 2018). In each pixel of the dataset the respective snow cover duration (SCD) is 22

depicted. It is defined by Xie et al. (2018) as “the total number of snow-covered days in each water year” (1. October to 30. September the following year). Therefore, regions with longer lasting snow cover appear lighter than regions with less lasting snow cover (Xie et al. 2017). I additionally calculated the annual average snow cover duration using the raster calculator in ArcMap.

2.3.10 Terrain Ruggedness Index (TRI) The Terrain Ruggedness Index (TRI) was developed by Riley et al. (1999). It represents the amount of elevation difference from one cell to its neighboring cells in a digital elevation model (Riley et al. 1999). The heterogeneity of a habitat can be an important variable in species distribution and habitat selection (Riley et al. 1999). It has been used by various authors (e.g. Nellemann and Thomsen 1994; Alexander et al. 2005) for habitat modelling. “The index is calculated within a 3 x 3 neighborhood derived by the equation: 1⁄ 푇푅퐼 = [(∑(푋푖푗 − 푋00)²] 2 where Xij is the elevation of each neighbor pixel to the center pixel

X00” (Alexander et al. 2005). Essentially, the higher the index number, the higher the ruggedness of the terrain. Riley et al. (1999) propose a breakdown of the index levels into seven categories:

• 0-80 m = level surface • 81-116 m = nearly level surface • 117-161 m = slightly rugged surface • 162-239 m = intermediately rugged surface • 240-497 m = moderately rugged surface • 498-958 m = highly rugged surface • 959-4367 m = extremely rugged surface

In order to calculate the TRI from the DEM for the region of Friuli Venezia Giulia a tool called “Terrain Ruggedness Index” was used in QGIS Software (QGIS Development Team 2019) (see Annex: Figure 24).

2.3.11 Natura 2000 Areas The data on Natura 2000 areas was downloaded from the governmental WebGIS of Friuli Venezia Giulia (http://irdat.regione.fvg.it/WebGIS/). It contains all areas protected under the European Directive 92/43/EEC and was last edited in September 2018. It was created on a scale of 1:5000 (Biodiversity Service 2018).

2.4 MAXENT-SETTINGS Merow et al. (2013) identified the following six key decisions about input data that have to be made carefully in order to ensure validity of the model. In general, each decision should be made according to the species’ biology, limits of the data and study goals (Merow et al. 2013).

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2.4.1 Background Data As a null-hypothesis I assumed that in each cell of the landscape the probability of occurrence of the species is the same (Merow et al. 2013). Thus, the background data is chosen at random from all cells.

2.4.2 Features From each chosen environmental factor, Maxent generates response curves using a mathematical operation, which results in “features” (Merow et al. 2013). There are five different feature types (linear, quadratic, product, threshold and hinge features), which are calculated depending on the sample size (Merow et al. 2013). When choosing a linear feature, the mean value of the environmental factor at the predicted place of occurrence of the species is matched to the mean value of the actual presence points of the species (Merow et al. 2013). A quadratic feature instead considers the variance of the environmental factor at the predicted points of occurrence in comparison to the presence points (Merow et al. 2013). A product feature binds the covariance of one environmental factor to other environmental factors (Merow et al. 2013). There are two more feature types: threshold and hinge features, which are more complex. The mathematical operation behind the features is explained in Merow et al. (2013) in more detail.

In general, the program chooses automatically which features to generate depending on sample size (Merow et al. 2013). If the input has >80 samples it calculates all features, except threshold features, which are turned off by default (Merow et al. 2013; Phillips 2017). In this study, the default setting was used, since the sample size was larger than 80. The model was calculated using all but threshold features.

2.4.3 Regularization The purpose of regularization is to create models that best fit the data (Phillips et al. 2006). Therefore, for each environmental factor the individual feature, that best fits the model is selected automatically by the program (Merow et al. 2013). The regularization coefficient for each feature class (linear, quadratic etc.) is set by the program but could be multiplied with a constant chosen by the user according to the specific requirements of the research question (Elith et al. 2011; Merow et al. 2013). For this study, I left the regularization coefficients in the default setting.

2.4.4 Sampling Bias When sampling for presence of a species a certain sampling bias should be expected, e.g. sampling effort might be bigger close to human settlements or infrastructure, such as roads or paths (Phillips et al. 2009; Merow et al. 2013). Hence, it is not certain if one observed the species in a certain environment because it prefers the conditions there or because it is the only environment, that was sampled (Phillips et al. 2009; Merow et al. 2013). In some macro areas, like MA9 (the Karst region near Trieste), sampling effort was more intense than in other, more remote regions. Such a sample selection bias can have a strong impact on the model performance (Phillips et al. 2009). However, random sampling does not necessarily need to

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be done in a random geospatial context but rather in a random environmental context (Merow et al. 2013).

There are several possibilities to account for sampling bias: one of them is based on Target Group Sampling (TGS) (Phillips et al. 2009; Merow et al. 2013). When sampling for the species of interest, the presence of taxonomically related species is recorded within the same dataset. The approach with TGS therefore assumes, that when the taxonomically related group was sampled, the presence of the target species would have also been detected, had it occurred there (Phillips et al. 2009; Merow et al. 2013).

The approach chosen for this study is to manipulate the background data according to the sampled environmental conditions rather than choosing background data from all available environmental conditions in the region (Phillips et al. 2009; Kramer-Schadt et al. 2013). By adding the same sample selection bias occurring in the species presence data to the background data, the maxent output shows the differentiation between the species presence and the background data (Dudík et al. 2005; Phillips et al. 2009). Phillips et al. (2009) show, that a model created with the same bias in presence data and background data can be interpreted similarly to an unbiased model. In this study, it was chosen to use the species protocolled during the snow & mud tracking survey as target group species. It is important that TGS uses the same sampling approach as when sampling for the target species (Phillips et al. 2009; Kramer-Schadt et al. 2013).

2.4.4.1 Target Group Background I chose an approach similar to Kramer-Schadt et al. (2013) to generate the biased layer for the background data. Over a grid of 2 x 2 km all species records were transformed into a raster dataset. Each occurrence of golden jackal increased the value of the pixel cell by 1. Every record that was a different species (target group species) increased the value of the cell by 0.01. For each cell the sum of its 8 neighboring cells (Moore Neighborhood or Queens Neighborhood) was calculated. This resulted in a map of sampling effort (Figure 6), which Phillips et al. (2009) refer to as “Target Group Background”, that can be used for accounting for the sample selection bias in Maxent.

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Figure 6: Target Group Background: sample effort depicted in the region of Friuli Venezia Giulia. The warmer the colors, the more samples have been taken for the golden jackal in this area.

2.4.5 Model Output The Maxent software has several output formats; all output formats are derived from the “raw” output, which represents the relative occurrence rate (ROR) of the species (Merow et al. 2013). The exact equation for the calculation of the derived outputs can be obtained from Phillips and Dudík (2008) and Merow et al. (2013).

The ROR describes the relative probability that a cell of the gridded dataset is contained in a dataset of presence points (Phillips et al. 2006; Merow et al. 2013). In this study, it was assumed that each sampled cell of the environmental dataset was sampled randomly for presence of the species. The model was created using the ‘logistic’ output of Maxent and therefore it is interpreted as habitat suitability model (Royle et al. 2012).

2.4.6 Interpretation of the output When generating the model, Maxent takes the environmental factors chosen by the user and creates features by using mathematical transformations (e.g. linear, quadratic, threshold) (Merow et al. 2013). The predicted ROR is then plotted against the value of each feature (Merow et al. 2013). The resulting response curves give information on the importance of each feature and provide a way to assess the validity of the model (Merow et al. 2013). To achieve

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the results, Maxent maximizes a gain function, which “corresponds to finding a model that can best differentiate presences from background locations” (Merow et al. 2013).

I created the model using a 15-fold cross validation approach, which means that the dataset was split into 15 sets of training and testing data. The model then was created calculating the median of the 15 different models. The presence points were split into a training dataset of an average 217.5 presence points and a testing dataset of an average 15.5 presence points. An average of 10217.5 background points was created by Maxent. They were randomly taken from the environmental dataset and make up the pseudo-absence dataset. I accounted for sampling bias by inserting the target group background.

Model performance was tested by several statistical tests: a test on average omission rate and predicted area, a test on receiver operating characteristic (ROC) and a jackknife test. The first test is a threshold dependent test on omission rate and predicted area, where the fraction of points, that do not lie in an area predicted suitable for the species, are drawn against the cumulative area of all suitable pixels in the model (Phillips et al. 2006).

The ROC is a threshold independent form of evaluating model performance, which gives a statement about the model fit to the data (Phillips et al. 2006). The area under the curve (AUC) shows the probability that presence sites rank higher than absence sites, or in this case background points (Phillips et al. 2009). Interpreting the AUC in the case of a model with presence only data is a little more difficult, since it rarely assumes the value of 1, which would mean total differentiation between presence and background data (Phillips et al. 2006; Phillips et al. 2009). Generally, AUC discriminates well when the value is >0.7 (Hosmer and Lemeshow 1989; Kramer-Schadt et al. 2013) and when the value is >0.8 it is considered a good model (Araujo et al. 2005).

The jackknife test was conducted on training gain, testing gain and AUC and gives in depth information on model performance of single environmental factors as well as information on model performance if the respective environmental factor is excluded.

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3 RESULTS

3.1 SNOW & MUD TRACKING The survey took place over two months, from 30. January 2019 until 02. April 2019 at a total of 29 days, in which we completed 110 transects (Figure 3). In total, we surveyed transects over 108 km. We documented 163 signs of presence of the red fox, 35 signs of presence of the golden jackal and 19 signs of presence of the grey wolf. This amounts to a total of 217 signs of presence of golden jackal, grey wolf and red fox (Table 3). The red fox was detected to about 1523 m, which is the highest point of detection of the three species. The grey wolf was found up to 1364 m and the golden jackal was detected up to an elevation of 1106 m.

Out of 110 transects, there were only 29 transects without the presence of the fox – hence it was present in 73.6 % of all transects. We did linear transects, covering distances of up to 10 km in one day but splitting the transects in shorter parts of 1-1.5 km for better comparability. When excluding transects, where the red fox was present in the previous or following transect (made on the same day), the number rises to 95.5 % fox presence. There were only 5 transects where grey wolf and/or golden jackal where present, where red foxes were absent. There was a total of 28 transects, where grey wolves and/or golden jackals were detected, which means the red fox was present in 82.15 % of all transects where golden jackal or grey wolf was found.

The golden jackal was found at significantly lower altitude than the red fox and the grey wolf (Figure 7). The median for golden jackals is 405.6 m, for red fox 1004.4 m and for grey wolf 997.3 m. The one-way ANOVA (Table 4) shows, that the results differ significantly from each other. The Tukey post-hoc analysis further confirms, that the golden jackal was found at significantly lower altitudes than both the red fox and the grey wolf (Table 5). There was no difference in altitude between the red fox and the grey wolf. There was however only little data available on the grey wolf, which could influence the results.

Table 3: Total number of samples from the snow & mud tracking survey in February and March 2019 for each of the species of interest. Total number (TN) of signs of presence and the highest (High) and lowest (Low) elevation, at which signs of presence were found, as well as average altitude (Mean) and standard deviation (SD) are shown.

Species TN High [m] Low [m] Mean SD Golden Jackal 35 1106.4 58.1 514.4 388.6 Grey Wolf 19 1364.0 152.5 906.4 342.9 Red Fox 163 1522.9 58.3 945.0 413.6

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Figure 7: This boxplot represents the distribution in altitude of the golden jackal (blue; median = 405.6 m), the red fox (orange; median = 1004.4 m) and the grey wolf (grey; median = 997.3 m) from the snow & mud tracking data.

Table 4: ANOVA for testing for a significant difference between the golden jackal, red fox and grey wolf in terms of altitudinal distribution from the snow & mud tracking data.

Df Sum Sq Mean Sq F value P Species 2 5288447 2644224 16.3 <0.001 Residuals 182 30016751 164927

Table 5: Tukey post-hoc test for testing significant differences in altitude between the golden jackal, the grey wolf and the red fox from the snow & mud tracking data.

Species diff lwr upr P Grey Wolf – Golden Jackal 392.02351 109.6130 674.4340 <0.005 Red Fox – Golden Jackal 430.56980 250.1289 611.0107 <0.001 Red Fox – Grey Wolf 38.54628 -208.7402 285.8327 0.93

Apart from the grey wolf, the golden jackal and the red fox, we collected data from 11 different mammals, which resulted in a total of 573 presence records (Table 6). The different species, which were found during the snow & mud tracking survey were used as target group species to calculate for sample bias for generating the habitat suitability model of the golden jackal. Some of those species were already identified by Giannatos (2004) to live near or in golden jackal territory.

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Table 6: Species chosen as target group species for establishing the target group background for the habitat suitability model for the golden jackal. The data was collected during the snow & mud tracking survey in February and March 2019 using the same method as for the grey wolf, the golden jackal and the red fox.

Number of Records Hare (Lepus spp.) 59 Chamois (Rupicapra rupicapra) 33 Red Deer (Cervus elaphus) 232 Fallow Deer (Dama dama) 2 Roe Deer (Capreolus capreolus) 142 Squirrel (Sciuridae) 14 Wild Boar (Sus scrofa) 16 Badger (Meles meles) 13 Brown Bear (Ursus arctos) 7 Marten (Martes spp.) 51 Cat (Felis sp.) 4 Total number 573

3.2 ACOUSTIC STIMULATION In total, 403 calling stations were visited from 2010-2019 (44 in only 2019). The calling stations were distributed across all macro areas, except for MA1, where no acoustic stimulation survey has been held yet (Figure 8).

We could detect three “hotspots” of golden jackal groups in three different macro areas (MA3, MA5 and MA9). Between the year 2011 and 2015 the success rate for detecting golden jackals through acoustic stimulation was low, even though sampling effort was great (Figure 9). From 2016, the success rate more than doubled. The year 2013 is the exception, however only 6 emissions were made that year in an area of known golden jackal presence.

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Figure 8: Distribution of the calling stations used in the acoustic stimulation survey (2010-2019). Yellow dots indicate calling stations where golden jackals responded, blue dots indicate calling stations without response by golden jackals. Not all calling stations were visited every year. This dataset represents the cumulative data from all

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Relative Relative NumberResponses of Emissions to [%] 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Overall Year

Figure 9: Number of responses of golden jackals relative to the number of emissions in percent from 2010-2019. Overall represents the mean of all years. 31

3.3 CAMERA TRAPPING In total, we placed 9 camera traps within the same timeframe as the snow & mud tracking survey (Table 7). The average deployment time of the camera traps was 23.1 days. Four of the camera traps were intended to capture the presence of grey wolves, the other five were intended for the golden jackal. The presence of the golden jackal was registered at three (out of nine) camera sites, two of which were stationed in the same area. No presence of the grey wolf was registered, even though we positioned cameras along wildlife passages where wolf tracks had been found before. The red fox was registered at seven out of nine camera sites.

Table 7: Camera traps deployed in the field with the respective deployment time in days during the timeframe of the snow & mud tracking survey. MA = macro area, GJ = golden jackal, RF = red fox, GW = grey wolf. If a camera trap had to be removed early from the field the reason is stated in the notes.

No. MA Location Animal Deployment Notes GJ RF GW Time [days] 1 1 Cansiglio X 30 2 1 Cansiglio X 30 3 3 X 26 4 3 Preone X X 26 5 3 Priuso X 45 6 5 Rivoli di X 14 Increased waterflow 7 6 X 6 Forestry works 8 6 Paularo 6 Forestry works 9 8 X X 25

One camera (No. 9) was placed along the river Torre (MA8), where golden jackals had answered to acoustic stimulation beforehand. The analyzed records cover approximately one month from February 23rd until March 19th (Figure 10). On February 28th we placed fish bait in front of the camera trap. After that, we were able to capture two golden jackals on the same picture, therefore visually confirming that there is at least a pair (Figure 11).

In MA3 we placed two camera traps (No. 3 and 4) along the Tagliamento river, which were only 15 m apart from each other. I analyzed the pictures from February 9th until March 6th (Figure 12). We put a dead roe deer in front of the camera trap on February 16th, which can explain the increased activity of the golden jackal and the high number of records. Interestingly, the red fox has never been photographed near the carcass, but only on the other camera trap 15 m away. In terms of diurnal activity, both species seem to be active mainly during the night and the dawn/dusk hours (Figure 13).

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Figure 10: Number of records of golden jackal (blue) and red fox (orange) on a camera trap along the Torre river in MA8. The x-axis corresponds to the specific date of the record. This graph represents the daily activity of the golden jackal and the red fox.

Figure 11: Two golden jackals photographed by a camera trap along the river Torre in MA8.

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Figure 12: Number of records of the camera 1 (C1) of the golden jackal (blue), camera 2 (C2) of the golden jackal (grey) and camera 2 (C2) of the red fox (orange) on two camera traps placed approximately 15 m apart in MA3 along the Tagliamento river. The x-axis corresponds to the specific date of the record. This graph represents the daily activity of the golden jackal and the red fox.

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Golden Jackal Red Fox

Figure 13: Diurnal activity of the golden jackal (blue) and the red fox (orange) pooled from all three camera traps (two from MA3 along the Tagliamento river and one from MA8 along the Torre river). 34

3.4 THE HABITAT SUITABILITY MODEL FOR THE GOLDEN JACKAL The habitat suitability model for the golden jackal is displayed in Figure 14. In MA9 in the south-east corner of the region (Gorizia) and along the Slovenian border (MA10) the suitability is high. The east-west orientated valley near Tolmezzo, where the headwater of the Tagliamento river is situated, also shows high suitability, which continues along the course of the whole river (MA3, MA4, MA5). The area of Magredi (MA2) seems to have a large portion of suitable habitat, especially in the southern part where the Meduna and the Cellina rivers meet. In general, areas of lower elevation, e.g. the plains in the south and the mountain valleys in the north, seem more suitable than the high alpine regions.

The presence points of the golden jackals all lie in highly suitable habitat (Figure 15). But there are still highly suitable areas where no presence records exist. For example, in the southern part of the Tagliamento river (MA5) the habitat suitability is high. The same applies for the area of Magredi near Pordenone (MA2) and Cansiglio (MA1) where only one sign of presence of golden jackals could be detected.

There is only one area in MA3 where we could record all three species of interest (Figure 16). There, the records of the grey wolf were found in very suitable habitat for the golden jackals. However, the grey wolves were found on the outer limits of the highly suitable golden jackal habitat near the Tagliamento river.

The red fox was found in the same habitat as grey wolves and golden jackals respectively. The area described before in MA3 was the only area, where we could detect all three species in one area. There was only one area in MA2 where we could only detect golden jackal and grey wolf. However, we could only perform mud tracking in this area and it was very dry and therefore challenging to find tracks of animals on the dirt roads.

Data for grey wolves and red foxes were gathered only through the snow & mud tracking survey. Therefore, we have no data on these animals in MA9 and MA10, where no snow or mud tracking was conducted.

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Figure 14: Habitat suitability model derived from the median of the 15-fold cross validated logistic output of Maxent for the golden jackal in Friuli Venezia Giulia. The more intense the color is displayed, the higher is the habitat suitability. 36

Figure 15: Habitat suitability model and presence records of golden jackals in Friuli Venezia Giulia. With more color intensity the habitat suitability increases. Yellow points indicate presence records of golden jackals from the snow & mud tracking and acoustic stimulation surveys. 37

Figure 16: Habitat suitability and presence records of the grey wolf (green), the red fox (blue) and the golden jackal (yellow). 38

I assumed that sites with a habitat suitability >0.5 are highly suitable habitat for the golden jackal. Beaches, sands and dunes (30.8 %) is the Corine Land Cover -Class with the highest proportion in the area of habitat suitability >0.5 (Figure 17). Broad-leaved forests (25.4 %) and mixed forests (15.5 %) are followed by coniferous forests (7.3 %), which points to forests being important structures for golden jackals. In terms of agriculture, areas with extensive management with significant parts of natural vegetation are have larger areas of suitable habitat than intensively farmed lands.

Bare rocks 0 Water courses 0.1 Water bodies 0.1 Construction sites 0.2 Road and rail network 0.2 Mineral extraction sites 0.3 Dump sites 0.3 Discontinous urban fabric 0.5 Vineyards 0.6 Moors and heathlands 0.6

Sparsely vegetated areas 0.8 Classes

- Industrial or commercial units 0.9

Natural grasslands 1.3 CLC Complex cultivation patterns 1.8 Pastures 2.0 Non-irrigated arable land 2.0 Transitional woodland-shrub 3.9 Land principally occupied by agriculture 5.3 Coniferous forest 7.3 Mixed forest 15.5 Broad-leaved forest 25.4 Beaches, sands and dunes 30.8 0 5 10 15 20 25 30 35 Relative Proportion of Land Cover (Corine) [%]

Figure 17: Composition of suitable habitat over a habitat suitability value of 0.5 and its share in area classified by Corine Land Cover in Friuli Venezia Giulia. Only classes with a minimum area of 0.1 km² were included in the analysis.

The gain is a value that can be compared to the goodness of fit in a generalized linear model (GLM) (Phillips 2017). It assumes a value with a minimum of 0 and then increases towards an asymptote (Phillips 2017). Hence, it is an indicator of how well the model is fit to the data. In this study, the regularized training gain is 0.6176 (Table 8). The testing gain is 0.524, which means the average likelihood of a presence point in the model is 1.68 times higher than that of a background point.

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Table 8: Statistical output on training and testing gain and AUC of the habitat suitability model for the golden jackal.

Training Samples 217.4667 Regularized Training Gain 0.6176 Unregularized Training Gain 0.8595 Training AUC 0.887 Testing Samples 15.5333 Test Gain 0.524 Test AUC 0.8228 Test AUC Standard Deviation 0.0409

I tested the model on its performance by running two different tests. The threshold dependent test on omission rate and predicted area (Figure 18) shows, that the mean omission rate is close to the predicted omission rate.

The receiver operating characteristic (ROC) is a threshold independent statistical test (Figure 19). The mean area under the curve (AUC), is 0.8228 with relatively low standard deviation of 0.0409 (see also Table 8). This means, that the model is very well fit to the data and that the pixels identified as suitable area are not chosen at random.

Figure 18: Threshold dependent test on model performance by evaluating omission and predicted area of the habitat suitability model of the golden jackal. The blue line represents the mean omission on test data with standard errors (yellow).

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Figure 19: ROC curve of the habitat suitability model of the golden jackal. The red line shows the mean AUC with standard errors (blue). The black line shows the random prediction.

In order to test the model for overfitting, Maxent gives some threshold statistics on the data. According to Radosavljevic and Anderson (2014) the threshold dependent omission rates can be compared to “theoretically anticipated levels of omission”. The minimum training presence threshold (MTP) gives the lowest value of suitability for pixels with presence data (Radosavljevic and Anderson 2014). This means, it gives a value for the least suitable conditions in which a presence was detected in the training dataset (Radosavljevic and Anderson 2014). Analogously, the 10-percentile training omission threshold (10TP) represents a value that excludes the lowest 10 % of presence points. These values can be compared to the theoretical values of omission rates. After Radosavljevic and Anderson (2014), the MTP should have a value of 0 omission, the 10TP should have a value of 10 % omission. For the model, the MTP of omission for the training dataset is 0, the 10TP of omission for the training dataset is 0.096, which is 9.6 %. Therefore, the model seems to be fit well to the data, without suffering from overfitting.

3.4.1 Influence of Environmental Factors For the habitat suitability model, the percent contribution of each single environmental factor was calculated (Table 9). It needs to be interpreted with care, since the correlation between the variables can be high. Therefore, the model could have the same output when the percent contribution of the environmental factors is different (Phillips 2017). Corine Land Cover (CLC), forest types and Corine Biotopes Habitats are the three environmental factors with the most contribution according to the calculations.

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Table 9: Variable contribution of each environmental factor to the habitat suitability model for the golden jackal in order of magnitude.

Environmental Factor Contribution [%] Corine Land Cover 26.57 Forest Types 19.31 Corine Biotopes Habitat 15.72 Natura 2000 10.27 Temperature SEP 7.63 Terrain Ruggedness Index 6.77 Temperature JUL 3.21 Temperature MAY 1.84 Elevation 1.78 Temperature JUN 1.46 Temperature APR 1.39 Temperature MAR 0.57 Precipitation JUN 0.45 Proximity to Rivers 0.38 Precipitation MAR 0.36 Precipitation AUG 0.33 Precipitation JAN 0.25 Snow Cover Duration 0.25 Temperature JAN 0.23 Temperature OCT 0.23 Annual avg. Temperature 0.19 Precipitation APR 0.19 Precipitation OCT 0.19 Temperature AUG 0.10 Precipitation SEP 0.08 Precipitation JUL 0.08 Precipitation DEC 0.05 Precipitation NOV 0.05 Annual avg. Precipitation 0.03 Temperature DEC 0.02 Precipitation FEB 0.01 Temperature JAN 0.01 Temperature NOV 0.003 Precipitation MAY 0.001

In order to get an overview of variable importance and to better understand variable contribution to the model, jackknife tests on training and testing gain as well as on AUC were performed (Table 10 and see also Annex: Figure 25). The environmental factor Corine Biotopes 42

Habitat seems to bring the most gain to the model as a single variable (training gain: 0.2469; testing gain: 0.3554). This means this environmental factor has the most information by itself. Within the training dataset, the environmental factor forest types lessens the gain by 0.0996 when excluded from the analysis to 0.518. This means that this environmental factor appears to have the most information that other environmental factors do not have. The jackknife test shows, that there is relatively high correlation between the environmental factors, because the model gain when one variable is omitted does not change significantly. Hence, the information of the missing environmental factor is present in other environmental factors as well. However, as stated above, with a machine learning approach this can be neglected. The results for the training and testing dataset considering the model gain with each respective variable being the only variable, as well as the model gain without the respective variable can be obtained from Table 10.

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Table 10: Training gain and testing gain as well as AUC of the jackknife test of the habitat suitability model for the golden jackal with each environmental factor being left out of the overall model (…Without Variable) and each variable being the only variable in order of magnitude of the training gain without variable.

Training Gain Training Gain as Testing Gain Testing Gain as AUC without AUC as Only Without Variable Only Variable Without Variable Only Variable Variable Variable Forest Types 0.52 0.14 0.46 0.06 0.79 0.62 Corine Biotopes Habitat 0.55 0.25 0.55 0.36 0.82 0.75 Corine Land Cover 0.57 0.2 0.48 0.25 0.81 0.72 Natura 2000 0.59 0.06 0.55 0.01 0.81 0.56 Elevation 0.59 0.05 0.54 0.19 0.82 0.68 Terrain Ruggedness Index 0.6 0.04 0.52 0.17 0.82 0.68 Temperature SEP 0.61 0.1 0.51 0.26 0.82 0.77 Temperature JUL 0.61 0.09 0.5 0.25 0.82 0.72 Temperature MAY 0.61 0.05 0.52 0.17 0.82 0.65 Precipitation JUN 0.61 0.03 0.52 0 0.82 0.61 Temperature AUG 0.62 0.06 0.52 0.16 0.82 0.63 Temperature JUN 0.62 0.05 0.52 0.22 0.82 0.73 Annual avg. Temperature 0.62 0.05 0.53 0.16 0.82 0.63 Temperature OCT 0.62 0.04 0.53 0.16 0.82 0.64 Temperature APR 0.62 0.04 0.51 0.13 0.82 0.62 Precipitation JAN 0.62 0.03 0.53 0.02 0.82 0.52 Temperature MAR 0.62 0.03 0.53 0.13 0.82 0.62 Temperature JAN 0.62 0.03 0.53 0.12 0.82 0.56 Precipitation FEB 0.62 0.03 0.52 0.08 0.82 0.56 Precipitation DEC 0.62 0.03 0.52 0.08 0.82 0.61 Precipitation NOV 0.62 0.03 0.53 0.01 0.82 0.5 Temperature FEB 0.62 0.02 0.53 0.11 0.82 0.57

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Training Gain Training Gain as Testing Gain Testing Gain as AUC without AUC as Only Without Variable Only Variable Without Variable Only Variable Variable Variable Precipitation OCT 0.62 0.02 0.52 -0.03 0.82 0.46 Precipitation JUL 0.62 0.02 0.51 0.1 0.82 0.57 Precipitation APR 0.62 0.02 0.52 0.04 0.82 0.56 Precipitation MAR 0.62 0.02 0.53 0.01 0.82 0.47 Annual avg. Precipitation 0.62 0.02 0.53 -0.05 0.82 0.48 Temperature NOV 0.62 0.02 0.53 0.1 0.82 0.6 Precipitation MAY 0.62 0.02 0.52 -0.07 0.82 0.44 Precipitation SEP 0.62 0.01 0.54 0.08 0.82 0.59 Temperature DEC 0.62 0.01 0.52 0.09 0.82 0.56 Snow Cover Duration 0.62 0.01 0.53 -0.05 0.82 0.43 Proximity to Rivers 0.62 0.01 0.53 0.03 0.82 0.61 Precipitation AUG 0.62 0 0.53 0.01 0.82 0.53

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Comparing the training gain without variable from the jackknife analysis (Table 10) with the percent contribution (Table 9) it can be seen that for example SCD brings relatively little gain to the model in all jackknife tests. It even has a slight negative gain in the testing dataset when used as only variable. However, when looking at Table 9 it can be found in the middle section of variable contribution. There are three more variables showing negative gain in the testing data: annual average precipitation, average precipitation for October and average precipitation for May. This means, that the model performs slightly worse than the null model obtained through uniform distribution (Phillips 2017).

In order to gain more information on the importance of single environmental factors in the habitat suitability model, response curves were created. Only response curves with a significant effect on the model are presented here, all other response curves can be found in the Annex (Figure 26).

The Terrain Ruggedness Index (TRI) has negative influence on the habitat suitability when its value increases (Figure 20). Since TRI is a continuous value, a response curve could be drawn, both for the overall model and for when TRI is the only environmental factor in a model. TRI, which also takes the surrounding terrain and slope into account, is a variable derived from the digital elevation model (DEM).

Therefore, the response curves of the DEM (Figure 21) behave similarly to the response curves of the TRI. However, the decline in habitat suitability is less rapid when DEM is the only environmental factor in the model. For both environmental factors, habitat suitability for the golden jackal decreases with higher elevation or increased ruggedness of the terrain respectively.

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0,7

0,6

0,5

0,4

0,3 Habitat Suitability Habitat

0,2

0,1

0 0 5 10 15 20 25 30 35 40 TRI

TRI as only variable TRI overall

Figure 20: Response curve for Terrain Ruggedness Index (TRI) for the habitat suitability model for the golden jackal. The blue line corresponds to the TRI being the only variable and the orange line corresponds to the influence of the TRI on the whole model.

0,8

0,7

0,6

0,5

0,4

0,3 HabitatSuitability

0,2

0,1

0 0 500 1000 1500 2000 2500 Elevation

DEM as only variable DEM overall

Figure 21: Response curve for DEM for the habitat suitability model of the golden jackal. The blue line corresponds to the DEM being the only variable and the orange line corresponds to the influence of the DEM on the whole model. 47

The environmental factor which brings the most gain to the model and is within the top three in terms of percent contribution is Corine Biotopes Habitats. It is a categorical variable, which is why no response curve could be drawn. Instead the habitat suitability can be shown for each category. In total there are 109 different categories (see Annex: Table 17, Table 18), which are used to build the model. Some of them show higher importance for habitat suitability than others. In the overall model (Table 11) forest categories (i.e. beech forests and spruce reforestation) show higher suitability, and therefore more importance, than in the model where Corine Biotopes Habitats is the only environmental factor (Table 12). Additionally, ruderal plant communities, which are found mostly along rivers have a high habitat suitability. In both Table 11 and Table 12 only the five categories with the highest habitat suitability are displayed.

Table 11: Most important categories within the Corine Biotopes Habitats environmental factor derived from the response curve of the overall habitat suitability model for the golden jackal. For the complete table see Annex Table 17.

Code Name Overall Habitat Suitability 41.112 Montane woodrush beech forests 0.92 87.2c Ruderal communities with autochthonous species 0.71 41.1C3a Beech forests 0.55 31.87 Woodland clearings 0.54 42.26 Spruce reforestation 0.49

Table 12: Most important categories within the Corine Biotopes Habitat environmental factor derived from the response curve. The values of habitat suitability are derived from a model where this is the only environmental factor considered. For the complete table see Annex: Table 18.

Code Name Habitat Suitability as Only Variable 41.112 Montane woodrush beech forest 0.90 87.2c Ruderal communities with autochthonous species 0.80 87.2b Ruderal communities with exotic species 0.80 24.21 Unvegetated gravel river banks 0.79 41.131 Wood melick beech forest 0.76

Table 13: Description of the first four CLC-classes and the corresponding habitat suitability derived from two response curves of the habitat suitability model. The overall habitat suitability is derived from the response curve of the overall model, the habitat suitability as only variable is derived from a model with only CLC as an environmental factor (see Annex: Table 19).

Code Name Overall Habitat Habitat Suitability as Only Suitability Variable 132 Dump sites 0.96 0.98 331 Beaches, sands, dunes 0.45 0.78 231 Pastures 0.39 0.59 321 Natural grasslands 0.27 0.58 48

The Corine Land Cover-class with the most gain is the category of dump sites, for the overall habitat suitability and as well for the habitat suitability as only variable (Table 13). The second category with high habitat suitability is beaches, dunes and sands. Natural grasslands and pastures also stand out in comparison to the rest of the categories. This results to some part correspond with the analysis made in ArcMap, where beaches, sands and dunes make up the habitat with the largest share in area (Figure 17).

The response curve of the environmental factor forest types shows both coniferous and broad-leaved forests with high habitat suitability. In the overall model, the category with the highest habitat suitability is afforestation of spruce forests (Table 14), which corresponds with the environmental factor of Corine Biotopes Habitats, where spruce reforestation is represented as well. When forest types is the only environmental factor considered in the model (Table 15), colline forests with oak (Quercus sp.) and Carpinus sp. occur within the categories of high habitat suitability. All other categories have a high habitat suitability in the overall model as well. In Table 14 and Table 15, only the first five categories with the highest habitat suitability are shown. In total there are 355 categories (see Annex: Table 20)

Table 14: Description of the environmental factor forest types and the respective habitat suitability of the relevant categories. This data is from the response curve of the overall habitat suitability model and only the first five categories are shown. For the complete table see Annex: Table 20.

Code Name Overall Habitat Suitability SN/MF3 Afforestation of spruce in Piceo-Abietetum on montane 0.92 mesic soils MF3 Piceo-Abietetum in montane mesic soils 0.89 BC0f Typical Carpinetum, var. with Fagus 0.82 FB2 Mesothermic Coriletum 0.75 DD2 Primitive Orno-Ostrietum with rocks 0.74

Table 15: Description of the environmental factor forest types and the respective habitat suitability of the relevant categories. This data is from the response curve of the habitat suitability model with only forest types as an environmental factor and only the first five categories are shown. For the complete table see Annex: Table 21.

Code Name Habitat Suitability as Only Variable SN/MF3 Afforestation of spruce in Piceo-Abietetum on montane 0.91 mesic soils DD2c Primitive Orno-Ostrietum with rocks, var. karst 0.87 RA/BB0 Colline Querco-Carpinetum with mixed Robinietum 0.87 BC0f Typical Carpinetum, var. with Fagus 0.86 FB2 Mesothermic Coriletum 0.84

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The habitat suitability model for the golden jackal was calculated using the borders of Natura 2000 areas as well. There were several Natura 2000 areas with a high habitat suitability index, the most important ones being habitats along rivers (Table 16). It can be seen that many of the areas with a high habitat suitability, already lie within a Natura 2000 protected area. However, there are areas, which are not protected by Natura 2000 with high habitat suitability as well, e.g. in MA3 (Carnia/upper Tagliamento).

Figure 22: Natura 2000 areas with areas of high habitat suitability >0.5 (blue) and Natura 2000 areas with areas of low habitat suitability (black) in Friuli Venezia Giulia.

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Table 16: Habitat suitability for Natura 2000 areas from the response curve of the habitat suitability model for the golden jackal. Overall habitat suitability was calculated from the overall model, habitat suitability as only variable was calculated using only Natura 2000 areas as environmental factor.

Name Overall Habitat Habitat Suitability Suitability as Only Variable Lake 0.85 0.90 Rio Bianco of Taipana and Gran Monte 0.49 0.72 River Lerada 0.41 0.81 Karst areas of Venezia Giulia 0.41 0.68 Julian Alps 0.36 0.56 Karst Triest and Gorizia 0.32 0.59 Riverbank of the Tagliamento 0.22 0.62 Carnian Alps 0.22 0.48 Cavana of Monfalcone 0.21 0.44 Confluence of river Torre and river Natisone 0.21 0.44 Friulan Dolomites 0.21 0.44 Forest of Cansiglio 0.21 0.44 Forest of Cansiglio 0.21 0.44 Gorge of Pradolino and Monte Mia 0.21 0.44 Gorge of Cellina river 0.21 0.44 Matajur 0.21 0.44 and Valcalda mountains 0.21 0.44 Peat bog of Casasola and Andreuzza 0.21 0.44 Valley of Rio Bianco and Malborghetto 0.21 0.44 Not designated 0.21 0.42 Magredi of Pordenone 0.20 0.65 Valley of middle Tagliamento 0.20 0.58

3.4.2 Possible Golden Jackal Density in Friuli Venezia Giulia For calculating the possible density of golden jackals in the region according to available suitable habitat, I chose all areas with a suitability >0.5 as high-density areas and areas between a suitability of 0.4-0.5 as low-density areas. According to Lanszki et al. (2018) the home range of a female golden jackal is around 12 km². In Israel, home ranges of golden jackals range from 6.6 ± 4.5 km² in densely settled areas to 21.2 ± 9.3 km² in more natural areas (Rotem et al. 2008). Therefore, I have used 7 km² for high-density areas and 10 km² for low- density areas respectively as an approximation to the expected home range of golden jackals in Friuli Venezia Giulia. The total area of available habitat with habitat suitability ≥ 0.5 is 210.35 km². Divided by the estimated home range of 7 km², the results show that there is space for 30 territorial golden jackal groups. The low-density area is 141.79 km². Divided by the 51

estimated home range of 10 km² another 14 groups of golden jackals could be sustained. Within one group, usually the reproductive pair and one helper are present. Therefore, I calculated that each group consists of 3 individuals. Hence, the suitable habitat (352.15 km² in total) in the region could support 45 groups of golden jackals, which corresponds to 135 individuals. This results in a density of 0.4 golden jackals/km² of suitable habitat (suitability index >0.4) and an overall density of 0.02 golden jackals/km² in the total region of Friuli Venezia Giulia (7706.4 km²).

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3.5 SIGNS OF INTERACTION OF THE TARGET SPECIES The dataset obtained from snow & mud tracking was too small for any statistical operation. Therefore, I displayed the results in a map, showing areas on a 3 x 3 km grid, where signs of presence of more than one target species were found (Figure 23). There are two areas in MA3, where all three species were present. Furthermore, there is only one area in MA2, where the red fox was not present, but golden jackals and grey wolves were.

Figure 23: Interaction of the grey wolf, the golden jackal and the red fox in Friuli Venezia Giulia derived from the snow & mud tracking dataset on a 3 x 3 km grid (dark green = grey wolf and golden jackal; light green = grey wolf and red fox; orange = golden jackal and red fox; red = grey wolf, golden jackal and red fox).

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4 DISCUSSION

4.1 HABITAT SUITABILITY MODEL FOR THE GOLDEN JACKAL

4.1.1 Methodological Critique The habitat suitability model generated with the software Maxent performs very well from a statistical point of view. However, non-invasive survey methods, such as snow tracking or camera trapping, are prone to detection errors: false positive, i.e. misidentification and false negatives, i.e. non detection of the species (Franklin et al. 2019). Depending on snow condition and their age, tracks can seem larger or smaller, resulting in one of the two types of errors. Identifying the golden jackal from a set of tracks of red fox can be challenging and sometimes impossible.

Nonetheless, detection errors can have a large impact on the estimate of the population (McKelvey et al. 2008; Franklin et al. 2019). Lozier et al. (2009) showed the potential gravity of such errors with their paper on the distribution of the Sasquatch (i.e. Bigfoot) in western North America. They took data from sightings of a fictional being (the Sasquatch) and generated a convincing model using Maxent. By doing that, they show, that a model can have a statistically valid output, even though the data is obviously full of errors (Lozier et al. 2009). Therefore, it is imperative to use reliable data for the models, especially when they are non- verifiable (Lozier et al. 2009). Nevertheless, by using models, such as the one generated with the Maxent software, one can account at least for missed detections (Guillera-Arroita et al. 2015; Franklin et al. 2019).

The snow & mud tracking dataset, especially data on the grey wolf, was too small to do statistically relevant analysis in the different macro areas. There is not enough data on stable populations of the grey wolf to incorporate the species as a habitat factor for modelling the habitat suitability for the golden jackal. Although we have collected samples of the grey wolf during the snow & mud tracking survey in 2019, they were mainly from dispersing individuals and therefore not found in a representative habitat.

The acoustic stimulation surveys have become increasingly successful in recent years. This could suggest a better knowledge of the golden jackals by the researchers, which makes them more successful in selecting the calling stations for the surveys. It could however also be an indicator for a rising population density of golden jackals in the region of Friuli Venezia Giulia, which would mean that they are more detectible because there simply are more territorial groups. Literature on their ongoing range expansion (Krofel et al. 2017; Newsome et al. 2017) and their reproductive success in the region (Lapini et al. 2018) strongly point to the second reason for the increased detectability. However, it probably is a mixture of both theories.

In the future, acoustic stimulation surveys could be held not only for the golden jackals, but also for grey wolves. If the population growth of the grey wolves in Friuli Venezia Giulia

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happens in a similar way to the populations in Germany (Reinhardt et al. 2019) or Switzerland (CHWolf 2019), one can expect a significant increase in established packs in the coming years.

Pairing the snow & mud tracking data with acoustic stimulation data makes sense, insofar as snow or mud tracking usually detects moving animals, which are not necessarily in a representative habitat (D'Eon 2001). On the other hand, when conducting acoustic stimulation surveys it is assumed that in most cases territorial animals answer to the playback (Šálek et al. 2014). Therefore, our data should well represent the habitat use of golden jackals, which can be another reason for the good performance of the model.

In this study, the amount of errors like misidentification of species (e.g. due to difficult snow conditions) or missed species (e.g. due to unidentifiable tracks) is possibly quite high but undetectable. One possible future solution to minimize errors could be to include the analysis of environmental DNA (eDNA) from snow samples. Franklin et al. (2019) used this method to successfully identify rare carnivores from snow samples. They state that this method can be used either to identify one target species or to identify several species that are present in the respective habitat (Franklin et al. 2019). However, it is not possible to identify single individuals or sex of a target species (Franklin et al. 2019). Nonetheless, including samples of eDNA to the snow tracking surveys in Friuli Venezia Giulia could give even more information on species abundance and could reduce errors like misidentification or missed species.

4.1.2 Critique of the Results We have found signs of presence of golden jackals on an average altitude of 514 m, which corresponds with the statements of Spassov and Acosta-Pankov (2019). They also suggest that golden jackals avoid high alpine territory in dispersal (Spassov and Acosta-Pankov 2019). This statement however may be challenged by the recent evidence of golden jackal in high alpine territory at 2350 m (GOJAGE 2019). In this study, the highest sample of golden jackals was obtained at 1106 m. Nevertheless, we found the majority of signs of presence of golden jackals at significantly lower altitudes than grey wolves or red foxes. These findings confirm the statement of Lapini et al. (2018), that golden jackals, although they can be found in higher altitudes, prefer to stay in low lands in habitats, agricultural areas and forests in lower elevations.

The calculations done with the Maxent software reveal several environmental factors, which influence the presence of the golden jackal. The response curves of the model to the terrain ruggedness index (TRI) and the elevation (DEM) both support the findings of the altitudinal statistic from the snow & mud tracking survey. With increasing altitude or ruggedness, the habitat suitability decreases for the golden jackal. But not only golden jackals have a low tolerance for rugged terrain. Even though Spassov and Acosta-Pankov (2019) state that grey wolves prefer high alpine habitat, they also are limited by a certain terrain ruggedness (Alexander et al. 2005).

The logistic output of Maxent is derived from the raw model, which represents the relative occurrence rate of the species (Merow et al. 2013). The assumption, that the samples of

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presence-only data are random in a spatial context is more reasonable in a statistical sense than the assumption that individuals have been sampled in a random manner (Merow et al. 2013). However, interpreting the logistic output of Maxent as probability of presence of a species is questionable (Merow et al. 2013). Therefore, the logistic model should be interpreted as index of habitat suitability rather than probability of presence (Merow et al. 2013).

Analyzing the results of the jackknife statistics, some of the environmental factors have negative gain in the testing dataset. This suggests, that the model could be less transferable e.g. for calculations of future circumstances with changing land-use or climate. Additionally, the testing data is a relatively small dataset and the conditions in the region vary greatly. Depending on which presence points were chosen for the testing dataset, this could account for the negative values in the gain. Furthermore, it could be an indicator, that monthly average precipitation values do not form a good predictor for habitat suitability for the species. In the future, one might think about seasonal precipitation or exchanging it with a different environmental factor.

The analysis of areas with habitat suitability >0.5 has revealed that the largest portions of area are beaches, sands and dunes. This land cover type occurs mainly along the large rivers of the region. Golden jackals often build their dens in sandy soils and therefore, this habitat type could provide important structures for the reproduction of the golden jackals. Environmental factors with information on Corine Land Cover, forest types and Corine Biotopes Habitats have the highest variable contribution in the model and therefore contain important information for the calculations. The environmental factor Corine Land Cover contains information on forest types in a relatively low resolution, only discriminating broad-leaved, coniferous and mixed forests. Therefore, it is important to include an environmental factor with higher resolution to the model, such as forest types or Corine Biotopes Habitats.

By analyzing the environmental factor Corine Land Cover, dump sites (0.3 % of the area with habitat suitability >0.5) seem to have the largest positive effect on the model. According to Corine Land Cover classification, dump sites include landfills of (industrial) waste, pools of waste water or mining sites (Kosztra et al. 2017). This shows, that golden jackals do not necessarily avoid anthropogenic structures and that they might even live off human waste materials. In fact, in this study golden jackals were found in close proximity to a dump site in MA2. Ćirović et al. (2016) show that golden jackals provide valuable ecosystem services through removal of animal remains from the ecosystem, especially in developing countries with poor waste management. Pyšková et al. (2016) observed golden jackals near airports or golf courses.

The Corine Land Cover-class beaches, sands and dunes (30.8 % of the area with habitat suitability >0.5) encloses the large gravel beds of Mediterranean riverbeds. This category therefore already appears in two different analyses, underlining the importance of such habitats. Natural grasslands (1.3 % of the area with habitat suitability >0.5), the third category, are open grasslands with shrub- and herbaceous vegetation and occasional trees. This area 56

can be used for extensive grazing of livestock, however intensively used areas for agriculture are excluded from this category. Pastures (2.0 % of the area with habitat suitability >0.5) on the other hand can be grasslands near agricultural structures that can be used either extensively or intensively. Hedges or draining ditches can also occur in this category.

Taking a closer look to the environmental factor Corine Biotopes Habitats the analysis points to shrub- and forest communities. The results show that the most important habitat type is montane woodrush beech forest. Those forests are typically composed solely of beech (Fagus sylvatica), or in combination with silver fir (Abies alba) and/or Norway spruce (Picea abies). This forest community occurs in montane areas (Commission of the European Communities 1991). Ruderal communities with either autochthonous or exotic species make up the second and third most important category. Ruderal communities generally grow on disturbed landscapes, for example due to river dynamics.

Golden jackals are known to occur close to water bodies. Therefore, it is not a surprise, that one of the categories with the highest habitat suitability is unvegetated gravel and river banks. Wood melick beech forests on the contrary to the aforementioned beech forest type, occurs in colline environment (Commission of the European Communities 1991). It consists mainly of beech and oak (Quercus sp.). The last category of the highly suitable forest types is willow and sea-buckthorn brush. This community occurs on higher gravel shores and it mainly consists of Salix sp., which is resistant to periodical flooding along the river (Commission of the European Communities 1991).

Three of the categories are related to proximity to rivers, which emphasizes the importance of an intact river system for the golden jackals. Furthermore, they may find suitable habitat in beech forests up until montane areas, which in the case of the southern alps could mean up until 1500-1700 m in altitude.

The analysis of the forest types also points to lowland communities with beech and spruce- silver fir communities. There seems to be no specific preference for coniferous or broad- leaved forest. Shrub communities with hazel (Corylus avellana) are also represented, as are shady forests like the Piceo-Abietetum (Autonome Provinz Bozen - Südtirol 2010). They seem to be confined to montane and colline forest communities, which correlates with the results from the analysis of Corine Biotopes Habitats. Spassov and Acosta-Pankov (2019) propose that golden jackals select for not too dense forests in lower altitudes, which can be confirmed by this study. I found results in terms of preferred vegetation types that differ from Lapini et al. (2011), however this might be due to different data on forests or different approach in analysis.

The analysis of Natura 2000 areas revealed, that areas with proximity to rivers have relatively high importance in terms of habitat suitability. Additionally, a lot of areas of high habitat suitability for the golden jackal are already under protection by the Natura 2000 scheme. There are however still areas of high habitat suitability, which are not protected, e.g. most of

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MA3 along the upper Tagliamento river. The habitat suitability model could be a tool to establish new protected areas in the future.

4.2 INTERACTION OF GREY WOLF, GOLDEN JACKAL AND RED FOX The camera traps were placed in the field to detect possible interactions of the three target species. We were only successful in photographing golden jackals and red foxes on the same camera traps. Since the data is very limited, we can only make assumptions about avoidance behavior and alterations in activity patterns for those two species. The diurnal activity pattern suggests similar activity for both species. Both species were detected mainly during the night and the hours of dusk and dawn (Figure 13). This supports the findings of Pecorella and Lapini (2014). Additionally, our data suggests, that red foxes engage in a behavior of avoidance where golden jackals are present (Figure 12).

Since we were only able to collect data from one site, only assumptions can be made. It seems, that red foxes use the same habitat as golden jackals, however they use it on different days rather than different times of the same day. This is in accordance with the study of Scheinin et al. (2006), who show similar results in a field experiment. Sergio and Hiraldo (2008) state that meso-predators have two choices of shift in behavior: either use the same habitat in a different spatio-temporal manner or change the activity pattern.

Interestingly, when we put a carcass of a roe deer in front of the camera, the only animals that fed on the carcass were golden jackals, although a red fox was detected by the other camera deployed close by on the same day as the golden jackals. This could support the theory, that golden jackals suppress red foxes in their core territory (Pecorella and Lapini 2014). It is however also possible, that the camera trap did not record the red fox when it visited the carcass. In general, the co-existence of predators in the same habitat depends on its resource richness (Scheinin et al. 2006). If there are enough resources, the smaller animal can feed on less productive patches than the larger animal (Scheinin et al. 2006).

The golden jackals are concentrated rather on the lower lying areas than higher, mountainous regions. This correlates also with the findings on TRI in the response curve of the model (Figure 20). On the other hand, the localities of the grey wolves seem to be concentrated in rather mountainous areas, with the exception of the pack resident in the Magredi area (MA2). In Bulgaria, golden jackals do not inhabit areas, where grey wolves are reproducing (Spassov and Acosta-Pankov 2019). Those areas are however quite unsuitable for the golden jackals (Spassov and Acosta-Pankov 2019).

The data on the grey wolf is limited and it has been collected on both residential packs or pairs and dispersing individuals. However, in all cases where golden jackals and grey wolves were identified in the same area, the golden jackal was only identified by the collection of scats. Therefore, the presence of golden jackal can only be confirmed by genetic analysis of the scats.

The Magredi (MA2) is one area, where grey wolves inhabit prime golden jackal habitat. In 2016 a golden jackal was killed by wolves, which can be interpreted as a sign of intraguild predation 58

(Lapini et al. 2018). Vendramin et al. (2018) state, that golden jackals had been present in the years before the first wolf reproduction, but in 2019 their presence could not be confirmed with acoustic stimulation. Lapini et al. (2018) also report, that golden jackals in MA2 seized to response to acoustic stimulation, but were still photographed in the area. The reason could be, that the golden jackals do not vocalize in this area, when the playback is played, although Spassov and Acosta-Pankov (2019) state that in Bulgaria golden jackals sometimes answered even when grey wolves were close by. One more possibility could be, that they occupy the margin of the grey wolf territory and were either too far away to hear the playback or we were not able to hear them. The third possibility would be that they were killed by the grey wolves, which is unlikely, since we found scats of golden jackals and they were captured on camera traps by Lapini et al. (2018). This area remains a very interesting study area, and further intensive research might answer the question of what happened to the golden jackals of Magredi.

No acoustic stimulation survey was performed in the forest of Cansiglio (MA1), where we found evidence of a new pair of grey wolves. With the increasing trend of golden jackals moving from the east to the west of the region, in the future it could become a very interesting place. The habitat suitability of the forest of Cansiglio is high for the golden jackal. However, until now we could not detect the presence of the species there.

The diet of golden jackals and grey wolves are quite different: grey wolves almost exclusively hunt large ungulates, such as red deer (Cervus elaphus), whereas the diet of the golden jackal is more varied, including small mammals, carrion and human waste. Therefore, the competition about resources is probably not a limiting factor for the distribution of the golden jackal (Krofel et al. 2017). It is likely, that the grey wolves have a top-down effect on the golden jackals and that they engage a behavior of avoidance facing intraguild predation (Krofel et al. 2017). Unfortunately, we have only one example in this study, that supports this theory. However, with the increasing population of grey wolves in Friuli Venezia Giulia the effect could become more perceivable.

In the area near Tolmezzo (MA3) the habitat suitability index is very high for the golden jackal, especially close to the Tagliamento river in the valley bottom. There have been signs of presence of grey wolves in the outer limits of the jackal habitat, but so far not along the Tagliamento river. In that area, the population of golden jackal seems to be thriving, whereas grey wolves stick to the higher altitude regions in the surrounding areas. This macro area (MA3) was also the only area, where we were able to detect all three species.

Generally, the red fox has a very high abundance in the region. We were able to detect it in most cases and the data from the hunting census suggest a large population size in the region. Similar to the hypothesis of Levi and Wilmers (2012), with an increasing wolf population that suppresses meso-predator populations, the red foxes could experience a form of release. Until now, the population of grey wolves however seems to be too small to really impact the red fox – both in positive or negative manner.

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The red fox was found in golden jackal habitat. However, the limited data from the camera trapping suggest that red foxes engage in a behavior of avoidance of areas where golden jackals are present. To be precise: they seem to use the same habitat as the golden jackals but on different days. Nevertheless, they keep their diurnal rhythm, with being active mostly in the night and dusk and dawn hours of the day. The competition for food with the golden jackal is more pronounced for the red foxes (in comparison to grey wolves and golden jackals) since they rely on very similar food sources. In the future with rising population numbers, the red fox could experience a top-down effect from the golden jackals in terms of competition about resources. Further studies need to be made to confirm this theory for the region of Friuli Venezia Giulia.

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5 CONCLUSION

The results of the analysis of habitat suitability strongly suggest that golden jackals select for habitats along the course of rivers, forests and agriculturally used areas. The forest types include colline and montane forests, which could mean there is suitable habitat for golden jackals up until 1500-1700 m

Habitats along rivers are of viable importance for the golden jackal populations. Altering the course of rivers, straightening of channels and modification of riparian vegetation could be another threat for the golden jackal. Additionally, it could be one of the reasons, why it is so successful in Friuli Venezia Giulia, since the rivers in this region still have a relatively natural flow and sediment regime. However, with increasing use of those structures, e.g. by industry or for recreation, golden jackals could be driven away.

Increasing intensity of agriculture could be a threat to the existence of the species, which could provoke interspecific conflicts with grey wolves and red foxes in the area (Šálek et al. 2014). The same thing could happen with increasing intensification of agriculture. In the south of the region, close to the Mediterranean Sea, the agricultural practice is already quite intense. The habitat suitability in the southern area, apart from areas close to the large rivers, is therefore also quite low. In the northern part of the plains, agricultural practices are still performed on rather small parcels with hedges or small woodlands surrounding the fields. This provides very heterogenous habitat for many species and a large edge effect, which the golden jackals can profit from (Šálek et al. 2014).

At the moment, the populations of the grey wolf are probably still too small in Friuli Venezia Giulia to have an effect on either of the smaller canids. With the red fox being a generalist species and with the grey wolf and the golden jackal differing in habitat use, i.e. the grey wolves preferring habitat situated at mountainous areas with higher elevation, there could be enough space in the region to fit all three species. On the other hand, in Friuli Venezia Giulia there is one example, where grey wolves inhabit prime golden jackal habitat. Vendramin et al. (2018) and Lapini et al. (2018) state, that golden jackals have been present in the years before wolf reproduction, but after 2016 their presence could not be confirmed with acoustic stimulation, which was also the case in this study. Therefore, they either moved on to areas marginal to the grey wolf territory, were killed by the grey wolves or are too shy to vocalize. However, this aspect further confirms the validity of the model, since even without many presence records, the habitat of Magredi is recognized as highly suitable for the golden jackals.

The red fox on the other hand could profit from the presence of a new apex predator. They could experience a form of basal-predator release due to suppression of the meso-predator by the apex predator, similar to the wolf-coyote-fox dynamic in North America (Levi and Wilmers 2012). As of now, the red fox is still highly abundant in the region. Further research is definitely required to get more insight on the species behavior facing intraguild predation from two larger predators.

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7 ANNEX

Figure 24: Terrain Ruggedness Index for Friuli Venezia Giulia calculated from the DEM with qGIS

Figure 25: Jackknife tests for training and testing gain as well as AUC of each environmental factor of the habitat suitability model for the golden jackal. Light blue bars show model gain without the variable, dark blue bars show model gain with each environmental factor as only variable. The red bar at the bottom shows the overall gain of the model. Note that the scale of the x-axis is different in all three plots. 72

Table 17: Complete results for the habitat suitability model of the golden jackal for Corine Biotopes Habitats with the habitat suitability derived from the overall model.

Code Name Overall Habitat Suitability 41.112 Montane woodrush beech forests 0.92 87.2c Ruderal communities with autochthonous species 0.71 41.1C3a Beech forests 0.55 31.87 Woodland clearings 0.54 42.26 Spruce reforestation 0.49 41.1C3b Beech forests 0.46 44.614 Italian poplar galleries 0.37 44.112 Willow and sea-buckthorn brush 0.34 24.21 Unvegetated river gravel banks 0.33 42.112 Neutrophilous beech-zone fir forests 0.33 41.81 Hop-hornbeam woods 0.30 34.753b Eastern sub-mediterranean dry grasslands 0.30 87.2b Ruderal communities with exotic species 0.30 24.221b Subalpine willowherb stream community 0.29 24.13 Grayling zone 0.27 41.131 Wood melick beech forests 0.27 81 Improved grasslands 0.26 34.752a Eastern sub-mediterranean dry grasslands 0.24 42.5g Scots pine forests 0.24 82.2 Field margin cropland 0.24 31.8B South-eastern sub-Mediterranean deciduous thickets 0.24 (Schibljak) 41.59 Insubian acidophilous oak forests 0.23 24.221a Subalpine willowherb stream community 0.22 34.753a Eastern sub-mediterranean dry grasslands 0.21 22.1 Fresh waters 0.21 22.2 Unvegetated muds or shingles 0.21 22.42 Rooted submerged vegetation 0.21 22.43 Rooting floating vegetation 0.21 22.44 Chandelier algae submerged carpets 0.21 24.15 Bream zone 0.21 24.221c Subalpine willowherb stream community 0.21 31.42 Alpenrose heaths 0.21 31.48 Hairy alpenrose heaths 0.21 31.52 Outer alpine dwarf mountain pine shrub 0.21 31.611 Alpine green alder shrub 0.21 31.6212 Alpine prostate willow brush 0.21 31.881 Juniper downs 0.21 31.8A2 Italo-Sicilian sub-Mediterranean deciduous thickets 0.21 31.8C Hazel thickets 0.21 31.8h ? 0.21 34.4 Thermophile forest fringes 0.21 34.752b Eastern sub-mediterranean dry grasslands 0.21 35.11 Mat-grass swards 0.21 73

36.311 Pyreneo-Alpine mesophile mat-grasslands 0.21 36.413a Southern rusty sedge grasslands 0.21 36.413b Southern rusty sedge grasslands 0.21 36.433 Cushion sedge carpets 0.21 36.52 Rough hawkbit pastures 0.21 37.31 Purple moorgrass meadows and related communities 0.21 37.71 Watercourse veils 0.21 37.81 Hercynio-Alpine tall herb communities 0.21 41.111 Collinar woodrush beech forests 0.21 41.133 Bittercress beech forests 0.21 41.1C4 Beech forests 0.21 41.2A1 Oak-hornbeam forests 0.21 41.39 Post-cultural ash woods 0.21 41.731 Northern Italian Quercus pubescens woods 0.21 41.9 Chestnut woods 0.21 41.B3 Montane and subalpine birch woods 0.21 42.121 Inner alpine calcicolous fir forests 0.21 42.13 Acidophilous silver fir forests 0.21 42.211a Bilberry spruce forests 0.21 42.211b Bilberry spruce forests 0.21 42.212 Tall herb subalpine forests 0.21 42.221 Acidophile montane inner Alpine spruce forests 0.21 42.222 Calciphile montane inner Alpine spruce forests 0.21 42.322 Limestone larch forests 0.21 42.34 Secondary larch formations 0.21 44.13 White willow gallery forests 0.21 44.21 Montane grey alder galleries 0.21 44.431 Illyrian ash-oak-alder forests 0.21 44.44 Po oak-ash-alder forests 0.21 44.911 Meso-eutrophic swamp alder woods 0.21 44.92 Mire willow scub 0.21 45.319a Illyrian holm-oak woodland 0.21 51.1 Near-natural raised bogs 0.21 53.11 Common reed beds 0.21 54.21 Black Bog-Rush Fens 0.21 61.11 Alpine siliceous screes 0.21 61.22 Alpine pennycress screes 0.21 61.23 Fine calcareous screes 0.21 61.31 Peri-Alpine thermophilous screes 0.21 61.5 ? 0.21 62.1114 Triestine karst cliffs 0.21 62.15a Alpine and sub-Mediterranean calcareous cliffs 0.21 62.15b Alpine and sub-Mediterranean calcareous cliffs 0.21 62.311 Pavements 0.21 67.1 ? 0.21 82.3 Extensive cultivation 0.21 83.11 Olive groves 0.21 83.15 Fruit orchards 0.21 74

83.21 Vineyards 0.21 83.31 Conifer plantations 0.21 83.321 Exotic conifer plantations 0.21 83.325 Other broad-leaved tree plantations 0.21 86.3 Active industrial sites 0.21 86.41 Quarries 0.21 87.2a Ruderal communities 0.21 89.1 Saline industrial lagoons and canals 0.21 89.2 Fresh-water industrial lagoons and canals 0.21 83.324 Locust tree plantations 0.20 41.43 Alpine and peri-alpine slope forests 0.20 85.1 Large parks 0.20 38.2 Lowland hay meadows 0.19 24.12 Trout zone 0.19 86.1 Towns 0.19 42.611 Alpine Pinus nigra forests 0.13 82.1 Unbroken intensive cropland 0.13 42.67 Black pine reforestation 0.10

Table 18: Complete results for the habitat suitability model of the golden jackal for Corine Biotopes Habitats as only variable.

Code Name Habitat Suitability as Only Variable 41.112 Montane woodrush beech forests 0.90 87.2c Ruderal communities with autochthonous species 0.80 87.2b Ruderal communities with exotic species 0.80 24.21 Unvegetated river gravel banks 0.79 41.131 Wood melick beech forests 0.76 44.112 Willow and sea-buckthorn brush 0.74 24.221b Subalpine willowherb stream community 0.70 44.614 Italian poplar galleries 0.68 42.5g Scots pine forests 0.68 24.13 Grayling zone 0.60 31.8B South-eastern sub-Mediterranean deciduous thickets 0.57 (Schibljak) 34.752a Eastern sub-mediterranean dry grasslands 0.57 31.87 Woodland clearings 0.55 34.753b Eastern sub-mediterranean dry grasslands 0.54 81 Improved grasslands 0.51 42.112 Neutrophilous beech-zone fir forests 0.48 41.59 Insubian acidophilous oak forests 0.45 41.1C3a Beech forests 0.44 34.753a Eastern sub-mediterranean dry grasslands 0.44 24.221a Subalpine willowherb stream community 0.43

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24.12 Trout zone 0.43 42.26 Spruce reforestation 0.42 41.731 Northern Italian Quercus pubescens woods 0.42 41.43 Alpine and peri-alpine slope forests 0.41 41.81 Hop-hornbeam woods 0.38 38.2 Lowland hay meadows 0.36 82.2 Field margin cropland 0.34 83.324 Locust tree plantations 0.33 22.1 Fresh waters 0.31 22.2 Unvegetated muds or shingles 0.31 22.42 Rooted submerged vegetation 0.31 51.1 Near-natural raised bogs 0.31 53.11 Common reed beds 0.31 54.21 Black Bog-Rush Fens 0.31 89.2 Fresh-water industrial lagoons and canals 0.31 22.43 Rooting floating vegetation 0.31 22.44 Chandelier algae submerged carpets 0.31 24.15 Bream zone 0.31 24.221c Subalpine willowherb stream community 0.31 31.42 Alpenrose heaths 0.31 31.48 Hairy alpenrose heaths 0.31 31.52 Outer alpine dwarf mountain pine shrub 0.31 31.611 Alpine green alder shrub 0.31 31.6212 Alpine prostate willow brush 0.31 31.881 Juniper downs 0.31 31.8A2 Italo-Sicilian sub-Mediterranean deciduous thickets 0.31 31.8C Hazel thickets 0.31 31.8h ? 0.31 34.4 Thermophile forest fringes 0.31 34.752b Eastern sub-mediterranean dry grasslands 0.31 35.11 Mat-grass swards 0.31 36.311 Pyreneo-Alpine mesophile mat-grasslands 0.31 36.413a Southern rusty sedge grasslands 0.31 36.413b Southern rusty sedge grasslands 0.31 36.433 Cushion sedge carpets 0.31 36.52 Rough hawkbit pastures 0.31 37.31 Purple moorgrass meadows and related communities 0.31 37.71 Watercourse veils 0.31 37.81 Hercynio-Alpine tall herb communities 0.31 41.111 Collinar woodrush beech forests 0.31 41.133 Bittercress beech forests 0.31

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41.1C4 Beech forests 0.31 41.2A1 Oak-hornbeam forests 0.31 41.39 Post-cultural ash woods 0.31 41.9 Chestnut woods 0.31 41.B3 Montane and subalpine birch woods 0.31 42.121 Inner alpine calcicolous fir forests 0.31 42.13 Acidophilous silver fir forests 0.31 42.211a Bilberry spruce forests 0.31 42.211b Bilberry spruce forests 0.31 42.212 Tall herb subalpine forests 0.31 42.221 Acidophile montane inner Alpine spruce forests 0.31 42.222 Calciphile montane inner Alpine spruce forests 0.31 42.322 Limestone larch forests 0.31 42.34 Secondary larch formations 0.31 44.13 White willow gallery forests 0.31 44.21 Montane grey alder galleries 0.31 44.431 Illyrian ash-oak-alder forests 0.31 44.44 Po oak-ash-alder forests 0.31 44.911 Meso-eutrophic swamp alder woods 0.31 44.92 Mire willow scub 0.31 45.319a Illyrian holm-oak woodland 0.31 61.11 Alpine siliceous screes 0.31 61.22 Alpine pennycress screes 0.31 61.23 Fine calcareous screes 0.31 61.31 Peri-Alpine thermophilous screes 0.31 61.5 ? 0.31 62.1114 Triestine karst cliffs 0.31 62.15a Alpine and sub-Mediterranean calcareous cliffs 0.31 62.15b Alpine and sub-Mediterranean calcareous cliffs 0.31 62.311 Pavements 0.31 67.1 ? 0.31 82.3 Extensive cultivation 0.31 83.11 Olive groves 0.31 83.15 Fruit orchards 0.31 83.21 Vineyards 0.31 83.31 Conifer plantations 0.31 83.321 Exotic conifer plantations 0.31 83.325 Other broad-leaved tree plantations 0.31 85.1 Large parks 0.31 86.3 Active industrial sites 0.31 86.41 Quarries 0.31

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87.2a Ruderal communities 0.31 89.1 Saline industrial lagoons and canals 0.31 41.1C3b Beech forests 0.31 42.611 Alpine Pinus nigra forests 0.28 86.1 Towns 0.23 82.1 Unbroken intensive cropland 0.20 42.67 Black pine reforestation 0.15

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Table 19: Full table of the single CLC-classes and the corresponding habitat suitability derived from two response curves of the habitat suitability model, ordered by magnitude of the Overall Habitat Suitability. The overall habitat suitability is derived from the response curve of the CLC environmental factor from the overall model, the habitat suitability as only variable is derived from a model with only CLC as an environmental factor.

Code Name Overall Habitat Habitat Suitability as Only Suitability Variable 132 Dump sites 0.96 0.98 331 Beaches, dunes, sands 0.45 0.78 231 Pastures 0.39 0.59 321 Natural grasslands 0.27 0.58 312 Coniferous forest 0.22 0.37 311 Broad-leaved forest 0.21 0.45 324 Transitional woodland- 0.19 0.49 shrub 112 Discontinuous urban 0.19 0.36 fabric 411 Inland marshes 0.19 0.36 421 Salt marshes 0.19 0.36 511 Water courses 0.19 0.36 332 Bare rocks 0.19 0.36 322 Moors and heathland 0.19 0.36 221 Vineyards 0.19 0.36 512 Water bodies 0.19 0.36 142 Sport and leisure 0.19 0.36 facilities 122 Road and rail network 0.19 0.36 associated land 121 Industrial or commercial 0.19 0.36 units 123 Port areas 0.19 0.36 124 Airports 0.19 0.36 131 Mineral extraction sites 0.19 0.36 313 Mixed forest 0.19 0.35 243 Land principally occupied 0.19 0.42 by agriculture, with significant areas of natural vegetation 211 Non-irrigated arable land 0.15 0.25 242 Complex cultivation 0.12 0.26 patterns 333 Sparsely vegetated areas 0.10 0.17

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Table 20: Complete table of the response curve for forest types as environmental factor for the overall habitat suitability model for the golden jackal.

Code Name Overall Habitat Suitability SN/MF3 Rimboschimento di abete rosso su piceo-abieteto dei suoli 0.92 mesici montano MF3 Piceo-abieteto dei suoli mesici montano 0.89 BC0f Carpineto tipico, var. con faggio 0.82 FB2 Corileto mesotermo 0.75 DD2 Orno-ostrieto primitivo di rupe 0.74 RA/BB0 Robinieto misto su querco-carpineto collinare 0.73 DD2c Orno-ostrieto primitivo di rupe, var. carsica 0.63 GH1 Faggeta montana tipica esalpica 0.53 IH1 Pineta di pino silvestre mesalpica tipica/primitiva 0.52 GH2 Faggeta montana tipica mesalpica 0.52 EC0 Aceri-frassineto tipico 0.50 DB0f Orno-ostrieto tipico, var. con faggio 0.50 CB0i Rovereto dei suoli acidi, var. esalpica interna 0.46 ED0 Aceri-frassineto con faggio 0.37 IG1s Pineta di pino silvestre esalpica tipica, var. submontana 0.36 Unforested Area 0.21 DC2t Ostrio-querceto a scotano, var. a terebinto 0.17 DC2 Ostrio-querceto a scotano 0.15 RA Robinieto puro su formazioni originarie non individuabili 0.14 AA0 Ostrio-lecceta 0.14 BB0 Querco-carpineto collinare 0.14 BC0 Carpineto tipico 0.14 BD0 Carpineto con frassino 0.14 BE0 Carpineto con ostria 0.14 BF0c Carpineto con cerro, var. carsica 0.14 CA1 Rovereto tipico carsico 0.14 CA2 Rovereto tipico collinare 0.14 CB0 Rovereto dei suoli acidi 0.14 CC0 Castagneto dei suoli xerici 0.14 CD0 Castagneto dei suoli mesici 0.14 CE0 Castagneto con frassino 0.14 CF0 Castagneto dei suoli acidi 0.14 DB0 Orno-ostrieto tipico 0.14 DB0c Orno-ostrieto tipico, var. con carpino bianco 0.14 DC1 Ostrio-querceto tipico 0.14

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DC1r Ostrio-querceto tipico, var. con rovere 0.14 DC2a Ostrio-querceto a scotano, var. con acero campestre 0.14 DD1 Orno-ostrieto primitivo di forra 0.14 DD2l Orno-ostrieto primitivo di rupe, var. con leccio 0.14 DD3 Orno-ostrieto primitivo di falda detritica 0.14 EA0 Aceri-tiglieto 0.14 EB0 Aceri-frassineto con ostria 0.14 EC0i Aceri-frassineto tipico, var. esalpica interna 0.14 EC0t Aceri-frassineto tipico, var. con tiglio 0.14 EE0 Aceri-frassineto con ontano nero 0.14 FB1 Corileto macrotermo 0.14 GA0 Faggeta submontana con ostria 0.14 GC0 Faggeta submontana dei suoli mesici carbonatici 0.14 GC0c Faggeta submontana dei suoli mesici carbonatici, var. con 0.14 carpino bianco GE0 Faggeta submontana dei suoli mesici silicatici 0.14 GE0a Faggeta submontana dei suoli mesici silicatici variante con 0.14 abete bianco GF0 Faggeta submontana dei suoli acidi 0.14 GG0 Faggeta montana dei suoli xerici 0.14 GH1a Faggeta montana tipica esalpica, var. con abete bianco 0.14 GH1r Faggeta montana tipica esalpica, var. con abete rosso 0.14 GL0 Faggeta montana dei suoli mesici 0.14 GM0 Faggeta altimontana tipica 0.14 GM0l Faggeta altimontana tipica, var. con larice 0.14 GM0r Faggeta altimontana tipica, var. con abete rosso 0.14 GN0 Faggeta subalpina 0.14 GP1 Faggeta primitiva di rupe 0.14 GP2 Faggeta primitiva di falda detritica 0.14 HA0 Mugheta macroterma 0.14 HB1 Mugheta mesoterma esomesalpica 0.14 HB2 Mugheta mesoterma mesoendalpica 0.14 HC1 Mugheta microterma dei suoli basici 0.14 HC2 Mugheta microterma dei suoli acidi carbonatici 0.14 IA1 Pineta di pino nero primitiva di rupe 0.14 IA2 Pineta di pino nero primitiva di falda detritica 0.14 IB0 Pineta di pino nero tipica 0.14 IB0m Pineta di pino nero tipica, var. mesalpica 0.14 IC0 Pineta di pino nero submontana con ostria 0.14 ID0 Pineta di pino nero con faggio 0.14 IE0 Pineta di pino nero montana con pino silvestre 0.14

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IG1 Pineta di pino silvestre esalpica tipica 0.14 IG2 Pineta di pino silvestre esalpica con faggio 0.14 IH2 Pineta di pino silvestre mesalpica con faggio e abete rosso 0.14 LA0 Piceo-faggeto primitivo 0.14 LB0 Piceo-faggeto dei suoli xerici 0.14 LB0a Piceo-faggeto dei suoli xerici, var. con abete bianco 0.14 LB0g Piceo-faggeto dei suoli xerici, var. su substrati gessosi 0.14 LB0l Piceo-faggeto dei suoli xerici, var. con larice 0.14 LC1 Piceo-faggeto dei suoli mesici carbonatici montano 0.14 LC1a Piceo-faggeto dei suoli mesici carbonatici montano, var. con 0.14 abete bianco LC1g Piceo-faggeto dei suoli mesici carbonatici montano, var. su 0.14 substrati gessosi LC1l Piceo-faggeto dei suoli mesici carbonatici montano, var. con 0.14 larice LC2 Piceo-faggeto dei suoli mesici carbonatici altimontano 0.14 LC2a Piceo-faggeto dei suoli mesici carbonatici altimontano, var. con 0.14 abete bianco LC2l Piceo-faggeto dei suoli mesici carbonatici altimontano, var. con 0.14 larice LD0 Piceo-faggeto dei suoli acidi 0.14 LD0b Piceo-faggeto dei suoli acidi, var. bassomontana 0.14 LE1 Piceo-faggeto dei suoli mesici montano 0.14 LE1b Piceo-faggeto dei suoli mesici montano, var. bassomontana 0.14 LE2 Piceo-faggeto dei suoli mesici altimontano 0.14 MA1 0.14 MA2 0.14 MB1 Abieti-piceo-faggeto dei substrati carbonatici montano 0.14 MB1b Abieti-piceo-faggeto dei substrati carbonatici montano, var. 0.14 bassomontana MB2 Abieti-piceo-faggeto dei substrati carbonatici altimontano 0.14 MC1 Abieti-piceo-faggeto dei suoli mesici montano 0.14 MC1b Abieti-piceo-faggeto dei suoli mesici montano, var. 0.14 bassomontana MC2 Abieti-piceo-faggeto dei suoli mesici altimontano 0.14 MD0 Abieti-piceo-faggeto altimontano dei suoli acidi 0.14 ME1 Piceo-abieteto dei substrati carbonatici dei suoli mesici 0.14 carbonatici ME1x Piceo-abieteto dei substrati carbonatici dei suoli mesici 0.14 carbonatici, var. dei suoli xerici ME2 Piceo-abieteto dei substrati carbonatici dei substrati gessosi 0.14

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MF2 Piceo-abieteto dei suoli mesici bassomontano 0.14 MG1 Piceo-abieteto dei suoli acidi montano 0.14 MG1b Piceo-abieteto dei suoli acidi montano, var. bassomontana 0.14 NA1 Pecceta dei substrati carbonatici altimontana 0.14 NA1l Pecceta dei substrati carbonatici altimontana, var. con larice 0.14 NA2 Pecceta dei substrati carbonatici subalpina 0.14 NB1 Pecceta montana dei suoli acidi tipica 0.14 NB1m Pecceta montana dei suoli acidi tipica, var. microterma 0.14 NB2 Pecceta montana dei suoli acidi in successione con faggeta 0.14 NC0 Pecceta altimontana e subalpina dei substrati silicatici 0.14 ND1 Pecceta di sostituzione dei substrati gessosi 0.14 ND2 Pecceta di sostituzione dei suoli mesici 0.14 ND2n Pecceta di sostituzione dei suoli mesici, var. ad evoluzione non 0.14 prevedibile ND3 Pecceta di sostituzione dei suoli acidi 0.14 NE1 Pecceta secondaria montana 0.14 NE2 Pecceta secondaria altimontana 0.14 NF1 Pecceta azonale su alluvioni 0.14 OA0 Lariceto primitivo 0.14 OB1 Lariceto tipico dei substrati carbonatici 0.14 OB2 Lariceto tipico dei substrati silicatici 0.14 PA0 Alneta di ontano verde 0.14 QA1 Saliceto a Salix caprea 0.14 QA2 Saliceto a Salix cinerea 0.14 QA5 Saliceto a Salix waldsteiniana 0.14 QD0 Formazione a pioppo tremulo 0.14 QZ formazioni golenali e ripariali 0.14 RA/CD0 Robinieto misto su castagneto dei suoli mesici 0.14 RA/CF0 Robinieto misto su castagneto dei suoli acidi 0.14 RA/DB0 Robinieto misto su orno-ostrieto tipico 0.14 RA/DC1 Robinieto misto su ostrio-querceto tipico 0.14 RA/DC2 Robinieto misto su ostrio-querceto a scotano 0.14 SI/DB0 Rimboschimento di pino su orno-ostrieto tipico 0.14 SI/DC2 Rimboschimento di pino su ostrio-querceto a scotano 0.14 SI/GB0 Rimboschimento di pino su faggeta submontana tipica 0.14 SI/GH1 Rimboschimento di pino silvestre su faggeta montana tipica 0.14 esalpica SI/XE Rimboschimento di pino silvestre su neocolonizzazione 0.14 esalpica SN/BB0 Rimboschimento di abete rosso su querco-carpineto collinare 0.14 SN/BC0 Rimboschimento di abete rosso su carpineto tipico 0.14

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SN/CD0 Rimboschimento di abete rosso su castagneto dei suoli mesici 0.14 SN/EC0 Rimboschimento di abete rosso su aceri-frassineto tipico 0.14 SN/EC0i Rimboschimento di abete rosso su aceri-frassineto tipico, var. 0.14 esalpica interna SN/ED0 Rimboschimento di abete rosso su aceri-frassineto con faggio 0.14 SN/GA0 Rimboschimento di abete rosso su faggeta submontana con 0.14 ostria SN/GB0 Rimboschimento di abete rosso su faggeta submontana tipica 0.14 SN/GC0 Rimboschimento di abete rosso su faggeta submontana dei 0.14 suoli mesici carbonatici SN/GE0 Rimboschimento di abete rosso su faggeta submontana dei 0.14 suoli mesici silicatici SN/GH1 Rimboschimento di abete rosso su faggeta montana tipica 0.14 esalpica SN/IC0 Rimboschimento di abete rosso su pineta di pino nero 0.14 submontana con ostria SN/LD0 Rimboschimento di abete rosso su piceo-faggeto dei suoli acidi 0.14 SN/LE1 Rimboschimento di abete rosso su piceo-faggeto dei suoli 0.14 mesici montano SN/MC1 Rimboschimento di abete rosso su abieti-piceo-faggeto dei 0.14 suoli mesici montano SN/NC0 Rimboschimento di abete rosso su pecceta altimontana e 0.14 subalpina dei substrati silicatici SN/ND2 Rimboschimento di abete rosso su pecceta di sostituzione dei 0.14 suoli mesici SN/NE2 Rimboschimento di abete rosso su pecceta secondaria 0.14 altimontana SO/DB0 Rimboschimento di larice su orno-ostrieto tipico 0.14 SO/GH1 Rimboschimento di larice su faggeta montana tipica esalpica 0.14 ST/DB0 Rimboschimento plurispecifico di conifere su orno-ostrieto 0.14 tipico ST/EA0 Rimboschimento plurispecifico di conifere su aceri-tiglieto 0.14 ST/EC0 Rimboschimento plurispecifico di conifere su aceri-frassineto 0.14 tipico ST/GA0 Rimboschimento plurispecifico di conifere su faggeta 0.14 submontana con ostria ST/GB0 Rimboschimento plurispecifico di conifere su faggeta 0.14 submontana tipica ST/GC0 Rimboschimento plurispecifico di conifere su faggeta 0.14 submontana dei suoli mesici carbonatici

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ST/GE0 Rimboschimento plurispecifico di conifere su faggeta 0.14 submontana dei suoli mesici silicatici ST/GH1 Rimboschimento plurispecifico di conifere su faggeta montana 0.14 tipica esalpica X/ neocolonizzazione mista latifoglie-conifere 0.14 XD/ Neocolonizzazione avanalpica su formazioni originarie non 0.14 individuabili XD/CD0 Neocolonizzazione avanalpica tendente al castagneto dei suoli 0.14 mesici XD/DB0 Neocolonizzazione avanalpica tendente all'orno-ostrieto tipico 0.14 XD/DC1 Neocolonizzazione avanalpica tendente all'ostrio-querceto 0.14 tipico XD/EA0 Neocolonizzazione avanalpica tendente all'aceri-tiglieto 0.14 XD/EC0 Neocolonizzazione avanalpica tendente all'aceri-frassineto 0.14 tipico XD/GE0 Neocolonizzazione avanalpica tendente alla faggeta 0.14 submontana dei suoli mesici silicatici XE/ Neocolonizzazione esalpica 0.14 XE/BD0 Neocolonizzazione esalpica tendente al carpineto con frassino 0.14 XE/DB0 Neocolonizzazione esalpica tendente all'orno-ostrieto tipico 0.14 XE/EA0 Neocolonizzazione esalpica tendente all'aceri-tiglieto 0.14 XE/EC0 Neocolonizzazione esalpica tendente all'aceri-frassineto tipico 0.14 XE/EC0i Neocolonizzazione esalpica tendente all'aceri-frassineto tipico, 0.14 var. esalpica interna XE/GA0 Neocolonizzazione esalpica tendente alla faggeta submontana 0.14 con ostria XE/GB0 Neocolonizzazione esalpica tendente alla faggeta submontana 0.14 tipica XE/GE0 Neocolonizzazione esalpica tendente alla faggeta submontana 0.14 dei suoli mesici silicatici XE/GH1 Neocolonizzazione esalpica tendente alla faggeta montana 0.14 tipica esalpica XF/IH2 Neocolonizzazione mesalpica tendente alla pineta di pino 0.14 silvestre mesalpica con faggio e abete rosso XN/ Neocolonizzazione a prevalenza di abete rosso 0.14 XO/ Neocolonizzazione a prevalenza di larice 0.14 XQ/ Neocolonizzazione a prevalenza di salici ed altre specie ripariali 0.14 DC2c Ostrio-querceto a scotano, var. con cerro 0.13 GB0 Faggeta submontana tipica 0.08

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Table 21: Complete table of response curve from the habitat suitability model for the golden jackal for the environmental factor forest types, when it is the only variable.

Code Name Habitat Suitability SN/MF3 Rimboschimento di abete rosso su piceo-abieteto dei suoli 0.91 mesici montano DD2c Orno-ostrieto primitivo di rupe, var. carsica 0.87 RA/BB0 Robinieto misto su querco-carpineto collinare 0.87 BC0f Carpineto tipico, var. con faggio 0.86 FB2 Corileto mesotermo 0.84 MF3 Piceo-abieteto dei suoli mesici montano 0.80 DD2 Orno-ostrieto primitivo di rupe 0.73 ED0 Aceri-frassineto con faggio 0.70 EC0 Aceri-frassineto tipico 0.60 GH2 Faggeta montana tipica mesalpica 0.55 IG1s Pineta di pino silvestre esalpica tipica, var. submontana 0.52 CB0i Rovereto dei suoli acidi, var. esalpica interna 0.49 GH1 Faggeta montana tipica esalpica 0.47 IH1 Pineta di pino silvestre mesalpica tipica/primitiva 0.46 Unforested area 0.45 DB0f Orno-ostrieto tipico, var. con faggio 0.43 DC2t Ostrio-querceto a scotano, var. a terebinto 0.40 RA Robinieto puro su formazioni originarie non individuabili 0.38 DC2 Ostrio-querceto a scotano 0.36 DC2c Ostrio-querceto a scotano, var. con cerro 0.23 AA0 Ostrio-lecceta 0.18 BB0 Querco-carpineto collinare 0.18 BC0 Carpineto tipico 0.18 BD0 Carpineto con frassino 0.18 BE0 Carpineto con ostria 0.18 BF0c Carpineto con cerro, var. carsica 0.18 CA1 Rovereto tipico carsico 0.18 CA2 Rovereto tipico collinare 0.18 CB0 Rovereto dei suoli acidi 0.18 CC0 Castagneto dei suoli xerici 0.18 CD0 Castagneto dei suoli mesici 0.18 CE0 Castagneto con frassino 0.18 CF0 Castagneto dei suoli acidi 0.18 DB0 Orno-ostrieto tipico 0.18 DB0c Orno-ostrieto tipico, var. con carpino bianco 0.18 DC1 Ostrio-querceto tipico 0.18 DC1r Ostrio-querceto tipico, var. con rovere 0.18

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DC2a Ostrio-querceto a scotano, var. con acero campestre 0.18 DD1 Orno-ostrieto primitivo di forra 0.18 DD2l Orno-ostrieto primitivo di rupe, var. con leccio 0.18 DD3 Orno-ostrieto primitivo di falda detritica 0.18 EA0 Aceri-tiglieto 0.18 EB0 Aceri-frassineto con ostria 0.18 EC0i Aceri-frassineto tipico, var. esalpica interna 0.18 EC0t Aceri-frassineto tipico, var. con tiglio 0.18 EE0 Aceri-frassineto con ontano nero 0.18 FB1 Corileto macrotermo 0.18 GA0 Faggeta submontana con ostria 0.18 GC0 Faggeta submontana dei suoli mesici carbonatici 0.18 GC0c Faggeta submontana dei suoli mesici carbonatici, var. con 0.18 carpino bianco GE0 Faggeta submontana dei suoli mesici silicatici 0.18 GE0a Faggeta submontana dei suoli mesici silicatici variante con 0.18 abete bianco GF0 Faggeta submontana dei suoli acidi 0.18 GG0 Faggeta montana dei suoli xerici 0.18 GH1a Faggeta montana tipica esalpica, var. con abete bianco 0.18 GH1r Faggeta montana tipica esalpica, var. con abete rosso 0.18 GL0 Faggeta montana dei suoli mesici 0.18 GM0 Faggeta altimontana tipica 0.18 GM0l Faggeta altimontana tipica, var. con larice 0.18 GM0r Faggeta altimontana tipica, var. con abete rosso 0.18 GN0 Faggeta subalpina 0.18 GP1 Faggeta primitiva di rupe 0.18 GP2 Faggeta primitiva di falda detritica 0.18 HA0 Mugheta macroterma 0.18 HB1 Mugheta mesoterma esomesalpica 0.18 HB2 Mugheta mesoterma mesoendalpica 0.18 HC1 Mugheta microterma dei suoli basici 0.18 HC2 Mugheta microterma dei suoli acidi carbonatici 0.18 IA1 Pineta di pino nero primitiva di rupe 0.18 IA2 Pineta di pino nero primitiva di falda detritica 0.18 IB0 Pineta di pino nero tipica 0.18 IB0m Pineta di pino nero tipica, var. mesalpica 0.18 IC0 Pineta di pino nero submontana con ostria 0.18 ID0 Pineta di pino nero con faggio 0.18 IE0 Pineta di pino nero montana con pino silvestre 0.18 IG1 Pineta di pino silvestre esalpica tipica 0.18

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IG2 Pineta di pino silvestre esalpica con faggio 0.18 IH2 Pineta di pino silvestre mesalpica con faggio e abete rosso 0.18 LA0 Piceo-faggeto primitivo 0.18 LB0 Piceo-faggeto dei suoli xerici 0.18 LB0a Piceo-faggeto dei suoli xerici, var. con abete bianco 0.18 LB0g Piceo-faggeto dei suoli xerici, var. su substrati gessosi 0.18 LB0l Piceo-faggeto dei suoli xerici, var. con larice 0.18 LC1 Piceo-faggeto dei suoli mesici carbonatici montano 0.18 LC1a Piceo-faggeto dei suoli mesici carbonatici montano, var. con 0.18 abete bianco LC1g Piceo-faggeto dei suoli mesici carbonatici montano, var. su 0.18 substrati gessosi LC1l Piceo-faggeto dei suoli mesici carbonatici montano, var. con 0.18 larice LC2 Piceo-faggeto dei suoli mesici carbonatici altimontano 0.18 LC2a Piceo-faggeto dei suoli mesici carbonatici altimontano, var. con 0.18 abete bianco LC2l Piceo-faggeto dei suoli mesici carbonatici altimontano, var. con 0.18 larice LD0 Piceo-faggeto dei suoli acidi 0.18 LD0b Piceo-faggeto dei suoli acidi, var. bassomontana 0.18 LE1 Piceo-faggeto dei suoli mesici montano 0.18 LE1b Piceo-faggeto dei suoli mesici montano, var. bassomontana 0.18 LE2 Piceo-faggeto dei suoli mesici altimontano 0.18 MA1 0.18 MA2 0.18 MB1 Abieti-piceo-faggeto dei substrati carbonatici montano 0.18 MB1b Abieti-piceo-faggeto dei substrati carbonatici montano, var. 0.18 bassomontana MB2 Abieti-piceo-faggeto dei substrati carbonatici altimontano 0.18 MC1 Abieti-piceo-faggeto dei suoli mesici montano 0.18 MC1b Abieti-piceo-faggeto dei suoli mesici montano, var. 0.18 bassomontana MC2 Abieti-piceo-faggeto dei suoli mesici altimontano 0.18 MD0 Abieti-piceo-faggeto altimontano dei suoli acidi 0.18 ME1 Piceo-abieteto dei substrati carbonatici dei suoli mesici 0.18 carbonatici ME1x Piceo-abieteto dei substrati carbonatici dei suoli mesici 0.18 carbonatici, var. dei suoli xerici ME2 Piceo-abieteto dei substrati carbonatici dei substrati gessosi 0.18 MF2 Piceo-abieteto dei suoli mesici bassomontano 0.18

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MG1 Piceo-abieteto dei suoli acidi montano 0.18 MG1b Piceo-abieteto dei suoli acidi montano, var. bassomontana 0.18 NA1 Pecceta dei substrati carbonatici altimontana 0.18 NA1l Pecceta dei substrati carbonatici altimontana, var. con larice 0.18 NA2 Pecceta dei substrati carbonatici subalpina 0.18 NB1 Pecceta montana dei suoli acidi tipica 0.18 NB1m Pecceta montana dei suoli acidi tipica, var. microterma 0.18 NB2 Pecceta montana dei suoli acidi in successione con faggeta 0.18 NC0 Pecceta altimontana e subalpina dei substrati silicatici 0.18 ND1 Pecceta di sostituzione dei substrati gessosi 0.18 ND2 Pecceta di sostituzione dei suoli mesici 0.18 ND2n Pecceta di sostituzione dei suoli mesici, var. ad evoluzione non 0.18 prevedibile ND3 Pecceta di sostituzione dei suoli acidi 0.18 NE1 Pecceta secondaria montana 0.18 NE2 Pecceta secondaria altimontana 0.18 NF1 Pecceta azonale su alluvioni 0.18 OA0 Lariceto primitivo 0.18 OB1 Lariceto tipico dei substrati carbonatici 0.18 OB2 Lariceto tipico dei substrati silicatici 0.18 PA0 Alneta di ontano verde 0.18 QA1 Saliceto a Salix caprea 0.18 QA2 Saliceto a Salix cinerea 0.18 QA5 Saliceto a Salix waldsteiniana 0.18 QD0 Formazione a pioppo tremulo 0.18 QZ formazioni golenali e ripariali 0.18 RA/CD0 Robinieto misto su castagneto dei suoli mesici 0.18 RA/CF0 Robinieto misto su castagneto dei suoli acidi 0.18 RA/DB0 Robinieto misto su orno-ostrieto tipico 0.18 RA/DC1 Robinieto misto su ostrio-querceto tipico 0.18 RA/DC2 Robinieto misto su ostrio-querceto a scotano 0.18 SI/DB0 Rimboschimento di pino su orno-ostrieto tipico 0.18 SI/DC2 Rimboschimento di pino su ostrio-querceto a scotano 0.18 SI/GB0 Rimboschimento di pino su faggeta submontana tipica 0.18 SI/GH1 Rimboschimento di pino silvestre su faggeta montana tipica 0.18 esalpica SI/XE Rimboschimento di pino silvestre su neocolonizzazione esalpica 0.18 SN/BB0 Rimboschimento di abete rosso su querco-carpineto collinare 0.18 SN/BC0 Rimboschimento di abete rosso su carpineto tipico 0.18 SN/CD0 Rimboschimento di abete rosso su castagneto dei suoli mesici 0.18 SN/EC0 Rimboschimento di abete rosso su aceri-frassineto tipico 0.18

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SN/EC0i Rimboschimento di abete rosso su aceri-frassineto tipico, var. 0.18 esalpica interna SN/ED0 Rimboschimento di abete rosso su aceri-frassineto con faggio 0.18 SN/GA0 Rimboschimento di abete rosso su faggeta submontana con 0.18 ostria SN/GB0 Rimboschimento di abete rosso su faggeta submontana tipica 0.18 SN/GC0 Rimboschimento di abete rosso su faggeta submontana dei 0.18 suoli mesici carbonatici SN/GE0 Rimboschimento di abete rosso su faggeta submontana dei 0.18 suoli mesici silicatici SN/GH1 Rimboschimento di abete rosso su faggeta montana tipica 0.18 esalpica SN/IC0 Rimboschimento di abete rosso su pineta di pino nero 0.18 submontana con ostria SN/LD0 Rimboschimento di abete rosso su piceo-faggeto dei suoli acidi 0.18 SN/LE1 Rimboschimento di abete rosso su piceo-faggeto dei suoli 0.18 mesici montano SN/MC1 Rimboschimento di abete rosso su abieti-piceo-faggeto dei suoli 0.18 mesici montano SN/NC0 Rimboschimento di abete rosso su pecceta altimontana e 0.18 subalpina dei substrati silicatici SN/ND2 Rimboschimento di abete rosso su pecceta di sostituzione dei 0.18 suoli mesici SN/NE2 Rimboschimento di abete rosso su pecceta secondaria 0.18 altimontana SO/DB0 Rimboschimento di larice su orno-ostrieto tipico 0.18 SO/GH1 Rimboschimento di larice su faggeta montana tipica esalpica 0.18 ST/DB0 Rimboschimento plurispecifico di conifere su orno-ostrieto 0.18 tipico ST/EA0 Rimboschimento plurispecifico di conifere su aceri-tiglieto 0.18 ST/EC0 Rimboschimento plurispecifico di conifere su aceri-frassineto 0.18 tipico ST/GA0 Rimboschimento plurispecifico di conifere su faggeta 0.18 submontana con ostria ST/GB0 Rimboschimento plurispecifico di conifere su faggeta 0.18 submontana tipica ST/GC0 Rimboschimento plurispecifico di conifere su faggeta 0.18 submontana dei suoli mesici carbonatici ST/GE0 Rimboschimento plurispecifico di conifere su faggeta 0.18 submontana dei suoli mesici silicatici

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ST/GH1 Rimboschimento plurispecifico di conifere su faggeta montana 0.18 tipica esalpica X/ neocolonizzazione mista latifoglie-conifere 0.18 XD/ Neocolonizzazione avanalpica su formazioni originarie non 0.18 individuabili XD/CD0 Neocolonizzazione avanalpica tendente al castagneto dei suoli 0.18 mesici XD/DB0 Neocolonizzazione avanalpica tendente all'orno-ostrieto tipico 0.18 XD/DC1 Neocolonizzazione avanalpica tendente all'ostrio-querceto 0.18 tipico XD/EA0 Neocolonizzazione avanalpica tendente all'aceri-tiglieto 0.18 XD/EC0 Neocolonizzazione avanalpica tendente all'aceri-frassineto 0.18 tipico XD/GE0 Neocolonizzazione avanalpica tendente alla faggeta 0.18 submontana dei suoli mesici silicatici XE/ Neocolonizzazione esalpica 0.18 XE/BD0 Neocolonizzazione esalpica tendente al carpineto con frassino 0.18 XE/DB0 Neocolonizzazione esalpica tendente all'orno-ostrieto tipico 0.18 XE/EA0 Neocolonizzazione esalpica tendente all'aceri-tiglieto 0.18 XE/EC0 Neocolonizzazione esalpica tendente all'aceri-frassineto tipico 0.18 XE/EC0i Neocolonizzazione esalpica tendente all'aceri-frassineto tipico, 0.18 var. esalpica interna XE/GA0 Neocolonizzazione esalpica tendente alla faggeta submontana 0.18 con ostria XE/GB0 Neocolonizzazione esalpica tendente alla faggeta submontana 0.18 tipica XE/GE0 Neocolonizzazione esalpica tendente alla faggeta submontana 0.18 dei suoli mesici silicatici XE/GH1 Neocolonizzazione esalpica tendente alla faggeta montana 0.18 tipica esalpica XF/IH2 Neocolonizzazione mesalpica tendente alla pineta di pino 0.18 silvestre mesalpica con faggio e abete rosso XN/ Neocolonizzazione a prevalenza di abete rosso 0.18 XO/ Neocolonizzazione a prevalenza di larice 0.18 XQ/ Neocolonizzazione a prevalenza di salici ed altre specie ripariali 0.18 GB0 Faggeta submontana tipica 0.12

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Figure 26: Response curve of the habitat suitability model of the golden jackal with average annual temperature, average annual precipitation, distance to rivers and average annual snow cover duration. The orange lines/bars correspond to the habitat suitability values of the respective environmental factors from the overall model. The blue lines/bars correspond to each environmental factor being the only variable.

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8 FINAL APPENDIX

Eidesstattliche Erklärung

Ich erkläre hiermit an Eides statt durch meine eigenhändige Unterschrift, dass ich die vorliegende Arbeit selbstständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Alle Stellen, die wörtlich oder inhaltlich den angegebenen Quellen entnommen wurden, sind als solche kenntlich gemacht.

Die vorliegende Arbeit wurde bisher in gleicher oder ähnlicher Form noch nicht als Magister- /Master-/Diplomarbeit/Dissertation eingereicht.

16.12.2019 Datum Unterschrift

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