Room for wolf comeback in the Netherlands

Transcription

1 Room for wolf comeback in the Netherlands A spatial analysis on the possibilities of settlement of wolves from European populations in the Netherlands G. Lelieveld 06 May 2012

2 Room for wolf comeback in the Netherlands A spatial analysis on the possibilities of settlement of wolves from European populations in the Netherlands Glenn Lelieveld 06 May 2012 Internship for MSc Ecology, Vrije Universiteit, Amsterdam Supervised by Roeland Vermeulen (FREE Nature) and Alfred Wagtendonk (IVM, Vrije Universiteit, Amsterdam) Abstract Cumulative effects of climate change, pollution, over-harvesting, fragmentation, degradation and loss of habitat threaten biodiversity globally. In Europe, centuries long over-harvesting and persecution of large herbivores and carnivores resulted in local extinctions of large carnivores. At the end of the 20 th century, legal protection resulted in stabilization and even increase of the European wolf populations. Now, the public debate forms a challenge in the recovery of wolf (Canis lupus) populations on the mainland of Europe. In West European countries, it is unclear where wolves can live after centuries of man manipulating the natural environment. To facilitate nature managers and policy makers to prepare for avoiding conflicts, it is important to know where wolves will settle in the Netherlands and what routes wolves will take to the Netherlands. Based on presence of artificial surfaces, water bodies, human population density, road density and prey distribution and abundance, a habitat suitability model and a cost-distance analysis were done. Prey species were identified as all large ungulates occurring in the Netherlands: roe deer, red deer, fallow deer and wild boar. Although the Netherlands is a dense populated country with high road density, wolves will still be able to find areas with low human disturbance and prey, mostly in the northeast of the Netherlands. There is room for at least 14 wolf packs. Although the French wolf population is less isolated, wolves from France and Germany will most likely migrate to the Netherlands via north and south of the Rhine-Ruhr metropolitan region. In a nearby area in the Netherlands a wolf was sighted in the summer of However, the large degree of plasticity that wolves show cannot be modelled. The areas where wolves can live according to this research should be seen as areas where wolves are most likely to settle first. To account for the left out parameters, the scenario is conservative. Therefore, the suitability for 14 wolf packs should be considered as an ecological minimum based on the parameters used in this study. In addition, when wolves settle in the Netherlands, the parameters of the habitat suitability analysis have to be refined.

3 Contents 1 Introduction Materials and methods Data collection Data quality Data preparation Data analysis Results Habitat suitability Migration routes Discussion Conclusion References Appendices... I Appendix I... I Appendix II... II Appendix III... III Appendix IV... IV Appendix V... V 3

4 1 Introduction Cumulative effects of climate change, pollution, over-harvesting, fragmentation, degradation and loss of habitat threaten biodiversity globally. (Pimm et al., 1995). In Europe, centuries-long over-harvesting and persecution of large herbivores and carnivores resulted in local, regional and global extinctions of large mammals, like auroch (Bos primigenius), tarpan (Equus ferus ferus), lynx (Lynx lynx), brown bear (Ursus arctos) and wolf (Canis lupus) (Berger et al., 2001; Szafer, 1968; Van Vuure, 2002; Vereshchagin and Baryshnikov, 1984). At the end of the 20 th century, legal protection and increasing ungulate populations resulted in stabilization and even increase of the European top predators (Franchimon et al., 2007; LCIE, 2004; Trouwborst, 2010). Especially relict wolf populations in the Italian Apennines and in Eastern Europe have increased in number and range rapidly (Breitenmoser, 1998). These populations are responsible for the return of wolves in the Alps, France and Germany (Randi, 2003). The German wolf population is estimated at 100 to 120 individuals in 2011 (KWL, 2012), the French wolf population is estimated at 180 to 200 individuals in 2009 (ONCFS, 2012). The wolves in the Alps are from the Italian, Balkan or the Carpathian wolf populations (NABU, 2012). The recolonizing wolves are juveniles of one or two years setting out to start new wolf packs in previously used or new patches (Huck et al., 2011). The choice for settling in new patches is classically determined by habitat quality and distance (Pulliam, 1988). This is also the case for wolves, but still some wolves migrate over large distances (Fabbri et al., 2007). In 2009, a one-year old radio-collared wolf named Alan dispersed 1500 kilometres from his home range near Nochten, Germany to Lithuania (Friedrich, 2010). In Scandinavia, large distance migrations of Russian wolves are held responsible for the survival of the population (Ellegren et al., 1996). Wolves use a wide range of habitats; low elevation forested areas with low levels of human interference are preferred (Fuller et al., 2003; Jedrzejewski et al., 2004; Kaartinen et al., 2005; Mladenoff et al., 2009; Wagner et al., 2012), although wolves do not avoid populated areas (Reinhardt and Kluth, 2007; Whittington et al., 2005). West-European wolf territories range from 100 to 300 square kilometre, averaging around 150 and 200 square kilometre (Jedrzejewski et al., 2007; Nowak et al., 2008). Wolf abundance and territory size are strongly influenced by prey availability (Jedrzejewski et al., 2002). Wolves in North American and tundra ecosystems have one or two prey species, in Europe wolves have multispecies prey communities, consisting of up to six species of ungulates (Fritts and Mech, 1981; Gasaway et al., 1992; Gasaway et al., 1983; Messier, 1991; Okarma, 1995). In the temperate and continental regions of Europe wolves specialize on one or two prey species, depending on the local circumstances such as densities of ungulate species (Barja, 2009; Jedrzejewski et al., 2000; Nowak et al., 2005; Nowak et al., 2011; Wagner et al., 2012). In forested areas, like the Bialowieza Primeval Forest, red deer are most abundant in wolf diet (Jedrzejewski et al., 2000), whereas in more open areas, roe deer were the most important food source (Barja, 2009; Nowak et al., 2005; Nowak et al., 2011; Wagner et al., 2012). Preference for a certain prey species influences the effects wolves have on ecosystems (Berger and Smith, 2005; Mech and Boitani, 2003; Paquet and Carbyn, 2003; Smith et al., 2003). Licht et al. (2010) summarized effects of wolves on ecosystems and society as: - limiting, and possibly regulating, the growth and abundance of prey populations; - removing weak, injured, or otherwise less-fit prey and alter sex and age ratios; - influencing prey behaviour, movement patterns, distribution, and habitat use; - creating a trophic cascade affecting the composition, structure, and functioning of plant communities, which in turn affects habitat availability for animals; - creating a trophic cascade affecting other biotic and abiotic resources, including water, soil, and geomorphology, which in turn affects habitat availability for other species; 4

5 - creating carrion that provides food for other species and cycles nutrients; - affecting the abundance, distribution, and behaviour of other animals through interspecific interactions; - increasing ecotourism and benefit local economies; - enhancing visitor experiences; - providing opportunities for scientific research. The impacts of above-mentioned effects can be large as wolves live more dense and are distributed more widespread than other large carnivores like lynx and brown bear (Herfindal et al., 2005; Mech and Tracy, 2004; Nowak et al., 2008; Stoen et al., 2006). In the wake of the recovery of wolves in Europe and North-America, this also leads to a rise in traditional conflicts between wolf and man, by loss of livestock and population decrease of game species (Baker et al., 2008; Enserink and Vogel, 2006; Mech, 1970; ONCFS, 2008; Reinhardt and Kluth, 2007). In some situations this led to culling of wolves to calm the public debate, despite legal protection (BBC, 2010; ONCFS, 2008). The most important motives to control large carnivore populations is fear (Linnell et al., 2002) and strong local socio-economic interests (Bjerke and Kaltenborn, 1999). In accordance with European legislation, national authorities use wolf management plans to avoid conflicts (NABU, 2012; ONCFS, 2008). The plans frequently include zoning strategies based on current and potential distribution of wolves. In the USA, Italy, Switzerland, Poland and Romania the potential distribution of wolves was modelled by use of habitat suitability models (Corsi et al., 1999; Glenz et al., 2001; Jedrzejewski et al., 2008; Jedrzejewski et al., 2004; Kaartinen et al., 2005; Maanen et al., 2006; Marucco et al., 2011; Massolo and Meriggi, 1998; Mladenoff et al., 1995; Mladenoff et al., 1999). These studies base the potential distribution of wolves on forest cover, road density, prey availability and human population density. As the niche breadth of wolves still is unknown, the parameters are determined on the present habitat use of wolves instead of its ecological potential (Mech, 1970; Pilot et al., 2010; Randi, 2011). Even as wolf populations are expanding in North America and Europe, present wolf distribution is still affected by historical persecution and long-term prey reduction (Fernandez and de Azua, 2010; Mladenoff et al., 2009; Mladenoff et al., 1999). Now wolves have been sighted several times in Belgium and the Netherlands (Maanen et al., 2006), there is debate whether these densely populated countries still have potential habitat for wolves. In this study, I examined which areas in the Netherlands are habitable for wolves considering human disturbance factors and prey abundance, and identified migration routes for wolves from West-European populations to the Netherlands considering human disturbance factors. The research questions in this study were: - What are ecologically suitable areas for wolves to live in the Netherlands? - Where are the ecologically suitable migration routes for wolves from West-European populations to the Netherlands? In this study, I defined ecological suitability as areas or routes that meet the prerequisites wolves need from their environment. This environment can be both natural and cultural, and is characterized by deleterious factors, such as presence of urban areas, water bodies, human population density and road density, and by beneficial factors, such as prey distribution and abundance. To answer the first research question I used a habitat suitability model with as parameters land use, human population density, road density and prey density. As ungulates are the most important prey species, I used prey densities of the four ungulates species occurring in the Netherlands; roe deer, red deer, fallow deer and wild boar. The second research question was answered by using a cost-distance analysis with the German and French populations as sources and the results of the first research question as the sink population. The parameters in the cost-distance analysis were land use, human population density and road density. Prey 5

6 availability was left out with the assumptions that wolves migrate primarily in areas that support at least a low density of prey and that wolves can effectively hunt prey in these areas. Both the habitat suitability model and the cost-distance analysis were executed in the geographical information system ArcGIS with the use of the Spatial Analyst package, both of ESRI. 6

7 2 Materials and methods 2.1 Data collection Data on spatial parameters contributing to dispersion and settlement of wolves were acquired via various organizations; see Table 1 for an overview on the sources and the characteristics of the used data. Table 1 Overview on the sources and characteristics of the used data Dataset Title Maker Year Acquired via Type Resolution Landuse (EU) CORINE 2006 European Environment Agency Raster 250 meter Landuse (NL) LGN4 Alterra (WUR) 2000 Institute for Environmental Studies of the VU, Amsterdam Raster 25 meter Human population Global Population Database LandScan 2009 Institute for Environmental Studies of the VU, Amsterdam Raster 30 arcsecond cell Road map (EU) Europe roads ESRI 2005 ESRI Data & Maps Polyline 1: Road map (NL) Topografische kaart Top10Vector Topografische Dienst Kadaster 2003 Institute for Environmental Studies of the VU, Amsterdam Polygon 1: Prey data (NL) Prey observations since 1905 Zoogdiervereniging 2011 Zoogdiervereniging Point 1000 meter Wolf data (DE) Aktuelle Rudelterritorien Wildbiologisches Büro LUPUS Document Wolf data (FR) Les données du Réseau Loup Office National de la Chasse et de la Faune Sauvage 2011 Quoi de neuf? no 24 Document 2.2 Data quality All spatial data used in this study came from third parties. It was therefore essential to assess the quality of the data to generate trust worthy outcomes with the analyses. As most data originated from authorities and have been used for some time, the important data quality factors for this study were the completeness and temporal accuracy. The Dutch prey dataset of the Zoogdiervereniging had some shortcomings. It is mostly based on volunteers passing on sightings. This is very cost-friendly, but is not as complete as when professionals would assess the population size and ranges. The second best solution would be a dynamic model of all prey species for the Netherlands. Dekker et al. (2010) made a model for wild boar (see also Appendix I), but this is not yet done for the other prey species. To prevent a deviation in the estimation of wild ungulate numbers per area, the modelled range of the wild boar was not used. 2.3 Data preparation Prior to the spatial analysis calculations, the raw data was converted into two databases, one for each of the study areas, the Netherlands and West Europe. The databases contain the same general data, though in a different extent and resolution. All data was summarized in the attribute table of one layer per database Dataset the Netherlands The LGN4 land use dataset was converted into a polygon grid with a 1 by 1-kilometre cell size. This layer was supplemented with a sample of the LandScan global population dataset as the human population density variable. Road density was added after intersecting the roads with the above-mentioned grid and calculating the length of the road sections. 7

8 All prey species counts per point in the period between 2000 and 2010 were generalized into a biomass value per point. Biomass as a function of the weight of wild boars, roe deer, red deer and fallow deer in the Netherlands was calculated as: biomass = N wild boars * 80 + N roe deer * 25 + N red deer * N fallow deer * 77.5, with N as number of individuals per point (Jedrzejewski et al., 2008; McElligott et al., 2001; Pettorelli et al., 2002). To correct for point counts, the biomass value was then extrapolated using a neighbourhood analysis, the focal statistics analysis, which calculated the maximum value in an annulus with an outer radius of 5 kilometres Dataset Europe Likewise as the data preparation for the habitat suitability, the CORINE land use dataset was converted into a polygon grid with a 5 by 5-kilometre cell size. This layer was supplemented with a sample of the LandScan global population dataset. Road density was added after intersecting the roads with the above-mentioned grid and calculating the length of the road sections divided by 25 to have a density per square kilometre. 2.4 Data analysis Habitat suitability The prepared habitat suitability dataset was summarised in one variable Suitability. A one by one kilometre cell was suitable (henceforth called prime area ) if it had less than 10 people living there, less than 400 meters road, at least 25 kilograms of biomass and the land use was neither water nor urban areas. These settings based on the characteristics of the Lausitz area in Germany and literature (Jedrzejewski et al., 2008; Jedrzejewski et al., 2004; Kaartinen et al., 2005). To allow patches to connect in one wolf territory, the prime wolf areas were extrapolated using a Euclidean distance metric with a maximum distance of 10 kilometres. This is the maximum distance wolves tend to visit outside of their home ranges (Merrill and Mech, 2003) and was visualized in four range classes (1, 2, 4 and 10 kilometre). These classes do not represent the suitability of the cells, but the distance to a prime area. In a fragmented landscape as that of the Netherlands, two scenarios were made assuming the wolves use the prime areas and an additional area surrounding it as territory. The distance of the two scenarios were based on the minimum additional space the cell sizes allow. In the first scenario wolf territories had to be in the prime area and a one-kilometre range, in the second scenario the range was set at two kilometres. The resulting patches of prime area with a certain range were assessed on size. Wolf territories in this study were assumed to be 225 square kilometres, which is larger than territory sizes found in Polish studies (Jedrzejewski et al., 2007; Nowak et al., 2008). As territory size is most primarily a function of prey density (Nowak et al., 2008), this compensates for the absence of accurate prey density data. The sensitivity of the model was analysed on changes in the threshold values of human population density and road density. In total 14 runs were done, based on seven different parameter values and the two scenarios (prime areas + 1 or 2 km buffer). The original threshold value of < 10 people & < 400 meters road per km 2 was set at 100%. The other parameter values were -50%, -30%, -10%, +10%, +30% and +50% compared to the 100%, resulting in a range from <5 people and <200 meters road per km 2 to <15 people and <600 meters road per km Migration routes Using the potential habitat of the first scenario (prime areas and a one-kilometre range) as a sink population and the German and French populations as source populations, a cost-distance analysis modelled the most suitable migration routes to the Netherlands. To compromise for the lack of literature on wolf migration, two 8

9 different scenarios were made. The different costs of two scenarios were based on the cumulative cost of road density, population density and land use (Marucco et al., 2011; Whittington et al., 2005). The first scenario assumes wolves are almost as strict in their suitability for migration as for habitat. The second scenario assumes that unfavourable land use and population densities hinder wolves only slightly and that major roads prove relatively more challenging. Table 2 and Table 3Table 3 below show the detailed cost for the two scenarios per factor per cell. Table 2 Detailed cost per factor per cell for the first scenario of the cost-distance analysis. Road length is in meters per 25 square kilometres, population density is number of inhabitants per 25 square kilometres. Cost Road length Population density Land use Forest, shrubs Agricultural land, open areas, wetlands > 5000 > 1500 Urban areas, water surfaces Table 3 Detailed cost per factor per cell for the second scenario of the cost-distance analysis. Road length is in meters per 25 square kilometres, population density is number of inhabitants per 25 square kilometres. Cost Road length Population density Land use Forest, shrubs Agricultural land, open areas, wetlands > 5000 > 3000 Urban areas, water surfaces 9

10 3 Results 3.1 Habitat suitability Ecological suitable areas for wolves in the Netherlands considering human disturbance and prey abundance were analysed with a habitat suitability analysis. This analysis, as shown in Figure 1 and in large in Appendix II, gives an impression on where ranging wolves in the Netherlands can find suitable areas and the connectivity between the prime areas, indicated by the different ranges. The map clearly shows that although the Netherlands is a dense populated country with high road density, wolves will still be able to find areas with low human disturbance and prey, in total 3524 square kilometre was identified as prime wolf area. Some of these prime areas will be difficult for wolves to reach, e.g. the Wadden Sea islands, and most of the prime areas are fragmented. The largest areas with the least fragmentation seem to lie in the central to northeastern parts of the Netherlands. These areas are also the least dense populated areas of the Netherlands. Figure 1 Distribution of prime areas and the classes of distance to nearest prime areas The habitat suitability analysis was executed for two possible scenarios of the distribution of wolf territories. The scenarios consist of the prime areas and a one-kilometre range, as shown in Figure 2 (and in large in Appendix III), and prime areas and a two-kilometre range, as shown in Figure 3 (and in large in Appendix IV). When these areas were analysed for potential territories of at least 225 square kilometre, the total area suitable for permanent presence of wolves is in the first scenario approximately 3750 square kilometre, which is large enough for 16 wolf packs. In the second scenario, the analysed area resulted in an area of approximately square kilometres, which is suitable for 44 wolf packs. Both scenarios show the same pattern of distribution of suitable areas: the coastline is not suitable, the Veluwe is suitable for multiple wolf packs and the largest suitable area is in the northeastern part of the Netherlands. In the second scenario, the southeast of the Netherlands has potential wolf habitat as well. 10

11 Figure 2 Distribution of potential wolf territories ( 225 km 2 ), based on prime areas and a one kilometre range Figure 3 Distribution of potential wolf territories ( 225 km 2 ), based on prime areas and a two kilometre range In the sensitivity analysis, I focussed on the change of suitable areas as prime area and the territory areas in the two scenarios and the influence of this on the amount of wolf packs. Figure 4 shows that the size of prime areas and scenario 1 territories increase more or less linearly with a higher threshold value, whereas the size of scenario 2 territories seems to top off. It also shows that at a threshold of <5 people and <200 meter road per square kilometre, the territories in scenario 1 is half the size of the normal threshold, whereas scenario 2 is affected less strongly Suitable area in km Prime area Scenario 1 Scenario Power of the parameter in % Figure 4 The change of suitable areas as prime area and the territory areas in the two scenarios at different strengths of the parameter. The normal parameter value of <10 people and <400 meter road per square kilometre was set at 100%. Figure 5 The effect of change in suitable territory area of the scenarios on the maximum number of wolf packs. The solid lines correspond with the assumption of 225 km 2 per wolf pack. For every solid line, the upper dashed line corresponds with assuming 200 km 2 per wolf territory and the lower dashed line with assuming 300 km 2 per wolf territory. 11

12 3.2 Migration routes The two scenarios of migration routes for wolves from West-European populations to the Netherlands, analysed by a cost-distance analysis, are shown in Figure 6 below and in Figure 7 on the next page. The colours in the maps indicate the costs of the routes between the sources and sink populations, considering human population density, land use and road density. As mentioned in chapter 2.4.2, the first scenario assumes wolves are almost as strict in their suitability for migration as for habitat, whereas the second scenario assumes that unfavourable land use and population densities hinder wolves only slightly and that major roads prove relatively more challenging. Both maps show that wolves from the French wolf population experience less costs to migrate than German wolfs. For example, the cost for French wolves to follow the river Rhine up north to the Rhine-Ruhr metropolitan region is comparable to the cost of German wolves migrating to the Czech Republic. That French wolves have better possibilities to migrate is confirmed by frequent sightings of wolves in the Vosges Mountains (Lichtfield, 2009). The path of the least resistance to the Netherlands would lead French wolves first to the north of the Rhine- Ruhr metropolitan region and then west to the Arnhem-Nijmegen region in the Netherlands. On three occasions in the late summer of 2011 several people stated to have seen a wolf in this region (Maanen, 2011). Wolves migrating from the German population will encounter more resistance due to unfavourable land use, almost exclusively agricultural lands and urban areas, in the west of Germany. Ü Figure 6 Analysis of wolf migration routes based on the first scenario by cost-distance analysis with the wolf populations in Germany and France as source (both indicated with a wolf and cub) and modelled wolf range in the Netherlands as sink population (all three areas in dark green). In red are the areas most difficult to reach for wolves. 12

13 Figure 7 Analysis of wolf migration routes by cost-distance analysis based on the second scenario with the wolf populations in Germany and France as source (both indicated with a wolf and cub) and modelled wolf range in the Netherlands as sink population (all three areas in dark green). In red are the areas most difficult to reach for wolves. 13

14 4 Discussion This study examined which areas in the Netherlands are habitable for wolves considering human disturbance factors and prey abundance. The habitat suitability analysis showed that the Netherlands has ecological potential for a wolf population. This corresponds with experiences in Poland, Germany and France of wolves having no difficulties living in cultural landscapes (Jedrzejewski et al., 2008). Telemetry study of the one year old wolf Alan showed that he had no problem avoiding humans when migrating from Germany to Belarus (Friedrich, 2010; Linnartz, 2012). However, the large degree of plasticity that wolves show was not incorporated in the model. The areas where wolves can live according to this research should be seen as areas where wolves are most likely to settle first. The habitat suitability analysis gives a prediction based on a few parameters. Left out parameters are chance of mortality by collision with vehicles based on traffic intensity, speed and knowledge of roads with high risk of collision with other large animals. Traffic is one of the major factors of wolf mortality (Blanco and Cortes, 2007; Gazzola et al., 2005; Mech, 2006a). Other factors influencing the settlement and migration of wolves can be local and temporal effects of wildlife crossings, fences, steep canals, other landscape elements and hunting and tourism activities (Melis et al., 2009). To account for the left out parameters, the first scenario is conservative. Therefore, the resulting suitability for 16 wolf packs should be considered as an ecological minimum based on the parameters and cell size used in this study. From the dispute between prone wolf scientists (Mech, 2006b, c; Mladenoff et al., 2006) it can be concluded that the accuracy of the predictive capacities of the habitat suitability model is vital. When investigating a species with high plasticity and high dispersal capabilities, the model needs fine-tuning to the characteristics the wolf population shows in the local setting (Chetkiewicz et al., 2006). As (Jedrzejewski et al., 2008) stated in the Polish study, In a real world, two similar patches of optimal habitat may have very different likelihoods of being occupied by a species due to their different connectivity to other patches and to the source population. The migration routes from source populations in Germany and France to the suitable habitats in the Netherlands was investigated in this study by cost-distance analysis. The suitable migration area in the Alps is in accordance with the habitat suitability model of the Alps (as shown in Appendix V) (Marucco et al., 2011). However, the analysis is done with large cell sizes and based on a few parameters. This implies that small-scale obstructions have not been taken into account. In addition, this type of analysis is sensitive to the settings of the parameters and the visualization, even though this effect was minimized as much as possible by performing two different scenarios. Therefore, the cost-distance analysis still gives an indication which areas are less accessible for wolves and which routes will be easier to migrate. The areas where wolves settle and migrate are not solely ecologically based. A small wolf population or even a single wolf can form a conflict when considering the viewpoints of the cattle-breeders. Society s attitude to wolves can be a problem, especially in West Europe where wolves recover after having long been exterminated. Here the level of human fear and intolerance of wolves is markedly higher than in regions where man and wolf have been coexisting for a long time (Linnell et al., 2003). It is in the best interest of the public and the wolves that conflicts are avoided by taking early action. 14

15 5 Conclusion This study examined which areas in the Netherlands are habitable for wolves considering human disturbance and prey abundance, and identified migration routes for wolves from West-European populations to the Netherlands considering human disturbance. The Netherlands has ecological suitable habitats for at least 16 wolf packs, mostly in the northeastern part of the country. Analysis of possible migration routes showed that wolves will most likely enter the Netherlands in the south-east. Therefore, an increase in wolf sightings is expected in the east of the Netherlands, followed by permanent recolonization of wolves in the Netherlands. What we should do until wolves recolonize the Netherlands is prepare for when they do. With confirmed wolf sightings in Belgium, the Netherlands is one of the last countries in West Europe to have recolonization of wolves. Therefore it is worthwhile to pay attention to the countries that have had to deal with recolonizing wolves for some years. By use of action plans, their governments successfully strive to take measures beneficial to the species, nature and society. 15

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19 Appendices Appendix I Modeled population range of wild boar (Sus scrofa) in the Netherlands Figure 8 Modeled population range of wild boar (Sus scrofa) in the Netherlands in orange. Sightings of wild boars in the period are visualized in grey. (Dekker et al., 2010) I

20 Appendix II Distribution of prime wolf areas and suitability ranges Figure 9 Distribution of prime wolf areas and suitability ranges II

21 Appendix III Distribution of wolf territories as in scenario 1 Figure 10 Distribution of wolf territories consisting of all non-water and non-urban areas within one kilometre of prime areas III