room for wolf comeback in the netherlands: rapport om ulvens muligheder i holland

7/29/2019 Room for wolf comeback in the Netherlands: Rapport om ulvens muligheder i Holland

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Room for wolf comeback in the Netherlands

A spatial analysis on the possibil ities of settlement of

wolves from European populations in the Netherlands

G. Lelieveld 06 May 2012


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Room for wolf comeback in the Netherlands

A spatial analysis on the possibil ities 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 20th

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 2011.

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.


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Contents

1 Introduction ......................................................................................................................... 4

2 Materials and methods ......................................................................................................... 7

2.1 Data collection................................................................................................................................. 7

2.2 Data quality ..................................................................................................................................... 7

2.3 Data preparation .............................................................................................................................. 7

2.4 Data analysis.................................................................................................................................... 8

3 Results................................................................................................................................ 10

3.1 Habitat suitability .......................................................................................................................... 10

3.2 Migration routes ............................................................................................................................ 12

4 Discussion........................................................................................................................... 14

5 Conclusion.......................................................................................................................... 15

References ................................................................................................................................. 16

Appendices................................................................................................................................... I

Appendix I .................................................................................................................................................... I

Appendix II.................................................................................................................................................. II

Appendix III ............................................................................................................................................... III

Appendix IV ............................................................................................................................................... IV

Appendix V ................................................................................................................................................. V


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1 IntroductionCumulative 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 20th

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 usedor 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, andgeomorphology, which in turn affects habitat availability for other species;


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- 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 morewidespread 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 andFrench 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


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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 9.3.1 with the use of

the Spatial Analyst package, both of ESRI.


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2 Materials and methods2.1 Data collectionData 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 2006European Environment

Agency2011 http://www.eea.europa.eu Raster 250 meter

Landuse (NL) LGN4 Alterra (WUR) 2000Institute for Environmental Studies

of the VU, AmsterdamRaster 25 meter

Human

population

Global Population

DatabaseLandScan 2009

Institute for Environmental Studies

of the VU, AmsterdamRaster

30 arc-

second cell

Road map (EU) Europe roads ESRI 2005 ESRI Data & Maps Polyline 1:10.000

Road map (NL) Topografische kaart

Top10Vector

Topografische Dienst

Kadaster2003 Institute for Environmental Studies

of the VU, AmsterdamPolygon 1:10.000

Prey data (NL)Prey observations

since 1905Zoogdiervereniging 2011 Zoogdiervereniging Point 1000 meter

Wolf data (DE)Aktuelle

Rudelterritorien

Wildbiologisches Bro

LUPUS2011

http://www.wolfsregion-

lausitz.de/aktuelle-rudelterritorienDocument

Wolf data (FR)Les donnes du

Rseau Loup

Office National de la Chasse

et de la Faune Sauvage2011 Quoi de neuf? no 24 Document

2.2

Data qualityAll 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 preparationPrior 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.

2.3.1 Dataset the NetherlandsThe 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.
http://www.eea.europa.eu/http://www.wolfsregion-lausitz.de/aktuelle-rudelterritorienhttp://www.wolfsregion-lausitz.de/aktuelle-rudelterritorienhttp://www.wolfsregion-lausitz.de/aktuelle-rudelterritorienhttp://www.wolfsregion-lausitz.de/aktuelle-rudelterritorienhttp://www.eea.europa.eu/


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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* 110 + 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 withan outer radius of 5 kilometres.

2.3.2 Dataset EuropeLikewise 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 analysis2.4.1 Habitat suitabilityThe 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 km2

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


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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

0 250 250 Forest, shrubs

1 250 - 750 250 375

2 750 1500 375 500 Agricultural land, open areas, wetlands

3 1500 2500 500 750

5 2500 5000 750 1500

10 > 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

0 250 375 Forest, shrubs

1 250 - 750 375 750 Agricultural land, open areas, wetlands

2 750 1500 750 1250

3 1500 2500 1250 2000

5 2500 5000 2000 300010 > 5000 > 3000 Urban areas, water surfaces


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3 Results3.1 Habitat suitabilityEcological 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. Someof 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.

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 10000 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.

Figure 1 Distribution of prime areas and the classes of distance

to nearest prime areas


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0

2000

4000

6000

8000

10000

12000

50 70 90 110 130 150

Suit

ableareainkm2

Power of the parameter in %

Prime area

Scenario 1

Scenario 2

Figure 2 Distribution of potential wolf territories (225 km2),

based on prime areas and a one kilometre range

Figure 3 Distribution of potential wolf territories (225 km2),

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


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3.2 Migration routesThe 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 scenarioassumes 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.

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.


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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.


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4 DiscussionThis 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 isconservative. 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. Societys 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.


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5 ConclusionThis 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.


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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 2000-2010 are visualized in grey. (Dekker

et al., 2010)


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Appendix II

Distribution of prime wolf areas and suitability ranges

Figure 9 Distribution of prime wolf areas and suitability ranges


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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


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Appendix IV

Distribution of wolf territories as in scenario 2

Figure 11 Distribution of wolf territories consisting of all non-water and non-urban areas within two kilometres of prime areas


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Appendix V

Habitat suitability for wolves in the Alps

Figure 12 Wolves habitat suitability map based on an occupancy analysis (Marucco 2011).

room for wolf comeback in the netherlands: rapport om ulvens muligheder i holland

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