brown bear habitat use pattern in greece

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Brown bear (Ursus arctos L.) habitat use patterns in two regions of northernPindos, Greece - management implicationsGeorgios Mertzanis a; Athanasios S. Kallimanis b; Nikolaos Kanellopoulos c; Stefanos P. Sgardelis b;Athanasios Tragos a; Ilias Aravidis d

a NGO “Callisto”, Thessaloniki, Greece b Department of Ecology, Aristotle University of Thessaloniki,Thessaloniki, Greece c Forestry Service, Metsovo, Greece d Development Agency of Thessaloniki,Thessaloniki, Greece

Online Publication Date: 01 January 2008

To cite this Article Mertzanis, Georgios, Kallimanis, Athanasios S., Kanellopoulos, Nikolaos, Sgardelis, Stefanos P., Tragos,Athanasios and Aravidis, Ilias(2008)'Brown bear (Ursus arctos L.) habitat use patterns in two regions of northern Pindos, Greece -management implications',Journal of Natural History,42:5,301 — 315

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Brown bear (Ursus arctos L.) habitat use patterns in two regions ofnorthern Pindos, Greece – management implications

Georgios Mertzanisa*, Athanasios S. Kallimanisb, Nikolaos Kanellopoulosc,

Stefanos P. Sgardelisb, Athanasios Tragosa and Ilias Aravidisd

aNGO ‘‘Callisto’’, Thessaloniki, Greece; bDepartment of Ecology, Aristotle University ofThessaloniki, Thessaloniki, Greece; cForestry Service, Metsovo, Greece; dDevelopment Agencyof Thessaloniki, Thessaloniki, Greece

Conservation of brown bear (Ursus arctos L.) depends upon protecting thespecies’ core habitat and mitigating the detrimental consequences of infrastruc-ture. Therefore, a detailed knowledge of bear habitat suitability is needed. Twosites were systematically surveyed in Pindos (Gramos and Grevena) for bearsigns. Additionally, in Gramos six individuals were monitored with conventionalVHF telemetry; and in Grevena a further six individuals were monitored withGPS-Simplex collars. The aim was to analyse the habitat preference of bearswithin their home ranges, and construct habitat suitability maps, using EcologicalNiche Factor Analysis. Despite individual behavioural differences of bears andintrinsic differences between the sites, the two regional population units exhibitsimilar habitat preferences. The bears prefer agricultural land and areas near theedge between forests and open landscape formations (grasslands, cultivated fields,fallow land). Bears use alpine meadows less than randomly expected. Our resultswere used in zoning proposals for Gramos and for improvement of mitigationmeasures of Via Egnatia highway stretch in Grevena.

Keywords: brown bear; habitat use; habitat suitability; telemetry; Pindos; Greece

Introduction

The brown bear (Ursus arctos L.) range in Greece consists of two distinct nuclei

located in the Pindos mountain range (NW Greece) and the Rodopi mountain

complex (NE Greece) (Mertzanis 1992, 1994). The total area of continuous bear

range is 13,500 km2. The Pindos population encompasses two regional population

units: one close to the Albanian border (including Gramos, Voio, Mali-Madi and

Triklari Mts.) and a larger one in the southern parts of Pindos range, mainly in the

Grevena, Ioannina and Trikala prefectures (Mertzanis 2002). During recent years,

brown bear populations in Pindos have exhibited a clear trend of expansion towards

the eastern and southern parts of the species’ former range (Mertzanis et al. 2006).

The minimum brown bear regional population size in Gramos is estimated at

between 34 and 41 individuals (Defiggou 2003) and in Grevena at 44 individuals

(Drosopoulou and Scouras 2005). However, brown bear conservation status in

Greece remains critical and faces major threats from human-caused mortality,

habitat fragmentation, habitat loss and habitat degradation (Mertzanis 1999, 2002).

Management decisions with reasonable expectancy of success and appropriate

management of threatened brown bear populations require a constantly updated

*Corresponding author. Email: [email protected]; [email protected]

Journal of Natural History

Vol. 42, Nos. 5–8, February 2008, 301–315

ISSN 0022-2933 print/ISSN 1464-5262 online

# 2008 Taylor & Francis

DOI: 10.1080/00222930701835175

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knowledge and adequate information of the ecological requirements and biological

needs of the brown bear, allowing specific management recommendations (Servheen

1994). To this extent, biological information on brown bear spatial behaviour (home

range and habitat use) is of high value (Mano 1994; Wooding and Hardisky 1994).

At a coarse scale, the habitat preferences of brown bears for high productivity

forests and areas with low human pressure have already been reported in other areas

of the species range in Europe (Kobler and Adamic 2000). However, this more

general approach is not always helpful enough when it comes to environmental

planning decisions, such as zoning for conservation of core bear habitats, designing

the optimal alignment of a major highway and appropriatly locating necessary

mitigation measures. To accomplish such specific objectives, a spatially explicit,

detailed knowledge of the brown bear’s ecology is needed. The aim of this paper is to

investigate the brown bear habitat preference within a site the entire extent of which

is characterized as suitable habitat, i.e. the habitat preference within the animals’

home range. The final stage of the analysis is a map of the site, where habitat

suitability is presented as a gradient from low to high, thus enabling environmental

managers to reach practical recommendations.

Materials and methods

Study sites

Gramos (Figure 1) is located in the mountains of Gramos and NW Voio, part of

Northern Pindos range, and is delineated southwards by the Sarantaporos river,

eastwards by the Aliakmon river and northwards by the border with Albania. The

study site covers approximately 850 km2 at altitudes ranging between 600 and 2520 m

asl. The largest part of the area is located in the watershed of the Sarantaporos and

Aliakmon rivers.

Approximately 48% of the study site is covered by dense forests, 13% by partially

forested areas and 26% by grasslands. Forest vegetation is composed of: 27% black

pine (Pinus nigra J.F. Arnold), 42% oak (Quercus sp.) and 26% beech (Fagus sp.).

The remaining area is covered by agricultural land, rocky outcrops and bare ground.

Mean monthly temperatures range from a minimum of 23.1u C to a maximum of

27.4u C. Mean annual precipitation is 814 mm. Nearly all native European mammal

species are present in the area, including the wolf (Canis lupus (L., 1758)), roe deer

(Capreolus capreolus (L., 1758)), chamois (Rupicapra rupicapra (L., 1758)), wild cat

(Felis sylvestris Schreber, 1775), wild boar (Sus scrofa L., 1758) and otter (Lutra lutra

(L., 1758)). The study site is remote, characterized by low human density and

scattered human settlements. A high density of forest road network (1.5 km/km2)

related to timber activities and a relatively high level of hunting pressure are among

the human-related negative disturbance factors. The study site includes one

proposed NATURA 2000 site (Koryfes Orous Gramos; GR 1320002), one

Biogenetic reserve (Flabouro-Barouga – covering 1.3 km2) and four wildlife reserves

covering a total of 100.5 km2.

The second study site, Grevena (Figure 1), extends over approximately 800 km2

of a mixed forest and agricultural ecosystem and is located in the northeastern part

of Pindos mountain range (Lyggos and Hassia mountain massifs). Of this area 75%

is forest, 10% meadows (pasture lands), 14% agricultural land, and low population

302 G. Mertzanis et al.


density human settlements occupy 0.3% of the total area. Major forest vegetation

types comprise oak (Quercus sp.), black pine (Pinus nigra) and beech (Fagus sp.). The

area is characterized by a mosaic of dense forests, openings and small scale

cultivations. Altitude ranges between 500 m and 2200 m. Mean monthly tempera-

tures range from a minimum of 23.4u C to a maximum of 28.2u C. Mean annual

precipitation (1990–1994) is 589 mm. Native European mammal species present in

Gramos are also present in this area. Part of the study site is included in the North

Pindos National Park.

Study animals and data collection methods

In Gramos, six brown bears were captured (four adult males, one sub-adult male and

one adult female) during the period 1997–1999 and 2000–2002 with an Aldrich Foot

Snare trap type and were sedated with KHCl/xylazine (Rompun). For the heavier

bears the initial volume injected by a blowpipe was 750mg/3ml with a booster volume

Figure 1. Map of brown bear distribution range in Greece and location of the study areas in

Pindos Mountain range.

Journal of Natural History 303


of 600 mg/3 ml (administered via intra-muscular hand administered injection). For the

smaller bears the initial volume injected by a blowpipe was 250 mg/3 ml with a booster

volume of 200 mg/3 ml (administered via intra-muscular injection). Time to

anaesthesia was usually less than 15 minutes. Total immobilization time was approx.

30–45 min. The bears were fitted with conventional radiocollars (MOD-500 NH from

Telonics, USA). Telemetry data (radiolocations) were collected using a VHF TR-4

receiver (Telonics S.A., USA), a directional (‘‘three-element’’– ‘‘Yagi’’) type antenna

(Telonics, USA), a magnetic compass and a portable GPS. For each bearing, the

‘‘Raised Antenna – Null Signal Average’’ (RA – NS) technique was used to locate each

individual (Springer 1979; Kenward 2001). The exact position of each individual was

defined by the triangulation method with a minimum of three bearings (Kanellopoulos

2002; Mertzanis et al. 2005). Ground telemetry data were collected with a frequency of

1/1.1 days in order to ensure statistical independence among locations. This frequency

is considered satisfactory and has been used by many researchers in Europe for the

study of free-ranging bear populations (Roth 1983; Huber and Roth 1986, 1993;

Clevenger et al. 1990; Kaczensky et al. 1994; Mertzanis 1999, 2005). Duration of the

bear monitoring period ranged from 2.5 to 13 months (average 9.5 months).

LOCATE II and GIS (ArcInfo, ArcView) software were used for data processing

and plotting. Telemetry data were analysed by daily groups of bearings. Geographic

coordinates of the bearing points by GPS were obtained and processed through

LOCATE II (Pacer Computer Software) for triangulation. For every triangulation

the ‘‘error areas’’ were calculated with the ‘‘Maximum Likelihood’’ estimator that

determines bear position to an accuracy of 95% (Nams 1989, 1990). The average

error area for the accepted triangulations was fixed to 0.5 km2 (with a maximum of

1.9 km2), a small surface compared to the home range of each individual in the study

area, and therefore not affecting the evaluation of habitat use (Nams 1989).

In Grevena, six brown bear adults were caught (four males and two females)

during 2003–2004 using the same capture and anaesthesia protocols. These bears

were fitted with GPS (TELEVILT) radio-collars with remote download system (RX-

900 TELEVILT receiver) and 12 hours VHF beaconing. The collars were set up to

give 15–17 positions daily (the effective positions averaged 6–10 daily). Bear presence

was recorded day and night with a similar frequency. The Grevena topography is

relatively smooth without deep ravines that can dramatically affect the GPS

efficiency. In the study area there is no information about forest cover affecting the

GPS efficiency and given that the percentage of the GPS records under dense forest

is comparable with that of the ground survey records, there is no reason to assume

that forest cover affected GPS efficiency. For Grevena GPS telemetry data

coordinates (x,y) were directly mapped on geo-referenced photo-maps of the study

site. Duration of bear monitoring period ranged from one to 20 months (average

7.2 months).

Both sampling protocols were combined with systematic ground surveys for

the collection of signs of bear presence and activity. Total length of sampling

transects (600 km in Gramos site and 1,008 Km in Grevena site) followed the dense

(1.5 km/km2) forest road network which is a common feature in both study areas.

Ground surveys looked for any signs of bears, including footprints, hairs and scats

that were either on or near the road network. The ground surveys were limited to the

forest road network, and therefore this information was not used to assess the bears’

habitat preference regarding distance from human structures. However, the ground



surveys systematically covered the entire extent of the study areas and all vegetation

and land use classes in proportion to their total availability in the landscape. Finally

the ground survey samples were unrelated to the telemetry locations because they

were collected at independent time periods and relate to a different and considerably

larger number of individuals. From the hairs and scats collected in Grevena, DNAwas extracted and 44 individuals were identified (Scouras and Drosopoulou 2005).

The 12 animals tracked with radio telemetry were followed for periods of

between 1 and 20 months. If the data are separated by season the number of active

individuals or number of radiolocations will drop significantly, especially for winter

as there are only a few individuals with restricted activity and few records. Therefore,

analysing the data by season would weaken the power of the statistical analysis.

Therefore, at this stage our dataset does not allow seasonal analysis of the bears’

habitat preference, since the data were not collected for this purpose.

Landscape cover

As the source data for landscape structure quantification, raster maps of the land

cover of the study sites were used. The vegetation cover was mapped by the forestry

service (Forest Management Plans of 1994 at a resolution of 1:20,000) and updated

based upon orthorectified aerial photographs of the area. In the case of Gramos the

raster resolution was 2006200 m2, while in Grevena it was 50650 m2. The difference

of the raster resolution was mainly due to the difference in accuracy of the VHF

telemetry data collected in Gramos and the satellite GPS telemetry data from

Grevena. Land use was considered for each cell, classified as dense forest, partiallyforested area, grassland meadows, bare land, cultivated fields, infrastructure and

surface water. Woodlands were further analysed based upon the dominant species,

and forests classified as oak (Quercus sp.), beech (Fagus sp.), black pine (Pinus

nigra), white pine (Pinus leucodermis), fir (Abies borisii regis) and mixed broadleaved

species.

The analysis took into consideration not only the contents of each cell in the

raster, but also the landscape composition of its spatial neighbourhood. The

landscape composition was quantified in neighbourhoods of different size aroundeach cell and the bears’ habitat preference examined in relation to neighbourhood.

More specifically, the radius of the neighbourhoods were defined at 600 m and

1000 m at Gramos and at 250 m and 450 m for Grevena. Then, the percentage of the

neighbourhood area each land use class and each forest type covered was measured.

Third, the diversity of the land cover classes and of the vegetation types in the

neighborhood was measured using the Shannon diversity index. This process was

repeated for each cell in the raster.

The land cover maps included information regarding villages and streams. Foreach cell in our raster, the distance from the nearest village and stream was estimated

using the spatial analyst of ArcGIS 9. Topographical information included

elevation, slope and aspect, and was obtained from a 100 m Digital Elevation

Model (DEM).

Statistical analysis

For the analysis of habitat selection location data from bear sign was used, as well as

telemetry data. The analysis was performed at two levels. At the first level, for each



background layer, the value at the location of the recorded bear presence was

estimated; the distribution of these values was compared with the distribution of

values for this layer in the entire landscape (presence versus availability) (Marcum

and Loftsgaarden 1980). The null hypothesis was that all values of the background

layer were equally suitable and that its frequency of use depended only on its

availability across the landscape. The entire extent of each study site was used as

background, because in both sites bears were observed throughout the area. Chi-

square tests were used to assess if the two distributions (background and species

presence) differed significantly statistically. When the two distributions differed

significantly, the Bonferroni 95% confidence intervals were estimated for the

frequency of bear use, for each class of the distribution, in order to identify which

classes were favoured or avoided (in accordance with Meriggi and Lovari 1996). If

the lower end of the 95% confidence interval for bear use was higher than the

availability then, this class was preferred; if the upper end of the 95% CI was lower

than the availability then this class was avoided, otherwise the class was used at

random according to its availability. The Bonferroni 95% confidence intervals were

estimated according to the formula:

pi{z0:05=2k

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

pi 1{pið Þn

r

vpivpizz0:05=2k

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

pi 1{pið Þn

r

where n is the total number of observations, pi is the proportion of bear presences in

the ith class of the variable, and z is the upper standard normal table value

corresponding to a probability tail area of 0.05/2k, with k the number of classes

considered.

This analysis allowed conclusions to be drawn regarding the preference or

avoidance for specific aspects of the landscape. It did not allow the building of a

habitat suitability map of the study site. To achieve that, a spatially explicit model

had to be constructed describing the relationship between the animals’ presence and

the habitat parameters. Three methods used in such modelling exercises are the

logistic regression (e.g. Mladenoff et al. 1999; Glenz et al. 2001), generalized additive

models (e.g. Guisan and Zimmermann 2000) and the classification tree analysis (e.g.

Jerina et al. 2003). The main drawback of these methods for the data is that they

require not only presence data, but also absence data. Such data (absences) may be

available for coarse scale analysis of habitat selection, e.g. bears are not observed in

the midst of towns or on the Greek islands; but they are impossible to collect for

analysis within the animals’ home range, as the one presented here. The lack of

recorded presence does not equal absence. The presence of the animal in a location

with suitable habitat may not be recorded either because of failure in detecting the

animal, or due to an animal’s effort to avoid the observers, or even due to the

idiosyncratic behaviour of each animal. Therefore an analysis method is needed that

relies only upon the recorded presences. Ecological niche factor analysis (ENFA)

was proposed to solve exactly this problem (Hirzel et al. 2002).

Ecological niche factor analysis starts off at the same point as the single variable

analysis, comparing the distribution of values where the animal is present with the

distribution of values in the background. ENFA relies on identifying differences in

the two distributions with respect to the mean (marginality) and with respect to the

standard deviation (specialization). This idea is applied to all variables in the study



related to topography, vegetation and land use, as well as the composition of the

spatial neighbourhood around each cell. The final habitat suitability is estimated

with the use of ordination techniques, such as principal component analysis. The

analysis estimates an overall marginality index, which expresses the difference

between the mean animal preference and the mean condition of the study site. Also,

the overall specialization index is estimated, which is a measure of the range of

environmental conditions the animal tolerates, compared to the range of values

recorded in the study site. For both indices, values close to 0 indicate a species which

can equally well utilize the entire area, and values close to 1 indicate a highly

specialized species that can only use a small part of the available landscape. The

analysis was performed using the Biomapper 3.0 software package (Hirzel et al.

2004).

In order to estimate the relative importance of the possible idiosyncratic

behaviour of individual bears, the analysis was repeated for each site, omitting one

individual at a time. The results were comparable, e.g. the marginality index for

Grevena varied from 0.28 to 0.32, and for Grammos from 0.51 to 0.56, and therefore

are not presented here.

Results

A total of 1299 radiolocations were obtained from sample (1) in the Gramos study

area, whereas 4564 radiolocations were obtained from sample (2) in the Grevena

study area (Table 1).

Telemetry data and total bear signs data (n5954 in Gramos and n51410 in

Grevena) suggest that bears can utilize the entire extent of both study sites, but they

appear to prefer some parts of the habitat more than others.

Even though the physiographic characteristics of the two sites appear similar

there are some significant differences. The Gramos region covers slightly higher

altitudes (600–2520 m asl) compared to the Grevena region (500–2200 m asl). In both

areas the bears utilize the entire area with the exception of the sites at high altitudes

Table 1. Bear telemetry sample main features from Gramos and Grevena study areas.

Bear Sex Age class Telemetry period Total radiolocations

Gramos sample

Nestoras male adult 10.5 months 276

Tsarnos male adult 10.5 months 266

Ondria female adult 2.5 months 68

Selinios male adult 13 months 423

Skotidas male adult 11 months 142

Zacharias male subadult 9 months 124

Grevena sample

Alexandros male adult 12 months 2060

Rohamis male adult 6 months 696

Maya female adult 2 months 164

Felicia female adult 20 months 1319

Aias male adult 1 month 175

Jeronymo male adult 2 months 150



(.1900 m asl), which are systematically avoided. Both areas, and especially Gramos,

are characterized by rugged terrain. However, in both sites, a preference for flat

areas and an avoidance of steep slopes were observed (.70%).

In both areas there is a rich network of rivers and streams. In both cases the

bears prefer locations close to surface waters. In Gramos, where the average distance

from rivers in the site is 646 m (with a maximum distance of 6288 m), bear locations

are on average 492 m away (with a maximum of 3300 m). In Grevena, where the

average distance from rivers in the site is 800 m (with a maximum distance of

4400 m), bear locations are on average 750 m away (with a maximum of 3100 m).

In both cases, there is a constant low density human presence, with several

villages located within the study sites. In Gramos, where the average distance from

villages in the site is 1994 m (with a maximum distance of 7500 m), bear locations are

on average 1850 m away (with a minimum of 50 m and a maximum of 6000 m). In

Grevena, where the average distance from villages in the site is 3020 m (with a

maximum distance of 8800 m), bear locations are on average 2600 m away (with a

minimum of 250 m and a maximum of 8300 m). In the case of villages in both sites

bears avoid areas close to villages (,500 m) but show a strong preference for

locations at intermediate distances. Crosschecks through field observations have

identified these locations to represent key food resources for bears (e.g. orchards,

fields, cultivated fields with cereals and fallow land).

The two study sites are dominated by woodlands (Table 2). However, the

landscape composition of the open space in the two sites differs significantly. In

Gramos grasslands are the main open space formation (26% of the site), followed by

cultivated land (7%) and bare land also present (4%). In Grevena cultivated fields are

the main open space formations (14%), followed by grasslands (10%) with bare land

barely present (,1%). In both sites, bears use dense woodlands as often as randomly

expected. However the use of the other habitat types differs. In Gramos, a slight

preference for grasslands and bare land was observed, and a slight avoidance of

cultivated fields and partially forested areas. In Grevena the opposite is true, with a

Table 2. The availability of the different land use classes in the two study sites (Gramos and

Grevena) and the frequency of bear presence in each land use class.

Land use class Gramos (X2, df55, p,0.001) Grevena (X2, df55, p,0.001)

Landscape

availability

(%)

Bear

presence

(%)

95% CI for

bear use

Landscape

availability

(%)

Bear

presence

(%)

95% CI for

bear use

Dense forests 47.9 47.2 (1063) 46.7–47.7 61.6 65.0 (3881) 64.8–65.2

Partially forested 13.8 9.8 (220) 9.5–10.1 13.8 12.9 (769) 12.6–13.2

Grasslands 26.4 30.8 (693) 30.3–31.3 9.9 8.8 (525) 8.6–9.0

Agricultural

land

7.4 5.7 (127) 5.5–5.9 14.3 13.4 (799) 13.2–13.6

Bare land 3.4 6.2 (139) 6.0–6.4 0.2 0.0 (0) 0.0–0.0

Infrastructure 0.7 0.5 (11) 0.4–0.6 0.3 0.0 (0) 0.0–0.0

Note: Bear presence data include both telemetry and bear signs. For each land use class, the

95% Bonferroni confidence interval for the frequency of bear presence is also presented. The

number of brackets is the absolute number of radiolocations.



slight preference for cultivated fields and partially forested areas, and an avoidance

of grasslands.

The species composition of the woodlands was further analysed (Table 3). In

both sites oak (Quercus sp.) forests are the dominant type. In Gramos the second

commonest forest type is beech (Fagus sp.), followed by black pine (Pinus nigra) anda low presence (,1%) of other coniferous and mixed broadleaved forests. In

Grevena, black pine is the second commonest forest type, and all other forest types

(beech, fir, pine and mixed broadleaves) are present. In both sites bears show a

preference for oak forests, and an avoidance of beech forests. Black pines are slightly

preferred in Gramos and slightly avoided in Grevena; while mixed broadleaved

forests are avoided in Gramos and preferred in Grevena.

Finally, special attention was paid to the land cover composition of the

neighbourhood around each cell. The percentage of the area covered by openlandscape formations (grasslands, cultivated land and bare land) influences the

habitat preference of the bears. In both sites, animals prefer locations that include

such formations in their neighbourhood, but avoid sites that either have no open

formations or that have predominately open formations in their neighbourhoods

(.90%). In other words, the bears prefer sites near the edge of grasslands and

cultivated fields, but avoid going to the centre of large patches of grasslands and

cultivated fields. The animals do not display any preference regarding the land cover

diversity of their spatial neighbourhood.

Taking into consideration all the available information, concerning the

topography, the vegetation and the land use in the sites, as well as the composition

of the spatial neighbourhood around each grid cell, the ecological niche factor

analysis (ENFA) was performed for the presence of bears in the two sites. Figure 2

Table 3. The availability of the different forest types in the two study sites (Gramos and

Grevena) and the frequency of bear presence in each forest type.

Forest type Gramos (X2, df55, p,0.001) Grevena (X2, df55, p,0.001)

Landscape

availability

(%)

Bear

presence

(%)

95% CI

for bear

use

Landscape

availability

(%)

Bear

presence

(%)

95% CI

for bear

use

Oak (Quercus sp.) 26.4 20.3 (457) 20.0–20.7 36.3 38.5 (2302) 38.2–38.8

Black pine (Pinus

nigra)

16.7 19.2 (433) 18.8–19.6 29.7 32.3 (1931) 32.0–32.6

Beech (Fagus sp.) 16.4 17.2 (387) 16.8–17.6 3.6 2.4 (144) 2.3–2.5

White pine (Pinus

leucodermis.)

1.0 0.1 (2) 0.07–0.13 3.6 0.3 (18) 0.2–0.4

Mixed

broadleaves

0.6 0.2 (5) 0.1–0.3 1.5 3.7 (222) 3.5–3.9

Fir (Abies sp.) 0.6 0.0 (0) 0.0–0.0 0.7 0.5 (30) 0.4–0.6

Open landscape

formations

38.0 43.0 (969) 42.5–43.5 24.6 22.2 (1327) 21.9–22.5

Note: Bear presence data include both telemetry and bear signs. For open landscape

formations we include all non-forested areas, i.e. grassland, agricultural land and bare land.

For each forest type, the 95% Bonferroni confidence interval for the frequency of bear

presence is also presented. The number in brackets is the absolute number of radiolocations.



shows the habitat suitability map produced for Gramos. In this case, the analysis

showed the species to score 0.55 in the marginality index, and 0.75 in the tolerance

index. This means that for bears a significant portion of the habitats in the site is of

high suitability but not all. As it can be seen in Figure 2, large parts of the landscape

remain underutilized. The model seems to capture the bear behaviour most

efficiently: approximately 77% of the bear presences are located in the areas

indicated as most suitable (the white areas of Figure 2) and only 13% in the least

suitable areas (black areas of Figure 2). Figure 3 shows the habitat suitability model

for Grevena, where a wider range of the available area is considered to be of high

suitability, and thus scores 0.37 in the marginality index, and 0.77 in the tolerance

index. In the case of Grevena the areas indicated by the ENFA model as most

suitable (white and light grey in Figure 3) host approximately 70% of the bear

presences, while the least suitable areas (black and dark grey in Figure 3) host only

6% of the presences. What is also interesting is that the model produced by the

analysis in the two sites is significantly different.

In Gramos the model is based on the composition of the land uses in the

neighbourhood around the point of interest, placing an emphasis on the availability

Figure 2. The habitat suitability map produced by the ecological niche factor analysis for the

Gramos study site. Habitat suitability is presented by a greyscale gradient. The lighter the

colour the more suitable habitat location for the bear.



of open landscape formations and partially forested areas and secondarily on the

species composition of the neighbourhood. In Grevena, the model is primarily based

on the distance from villages and then on the species composition of the

neighbourhood around the focal cell (mainly the availability of oak and pine

forests). However, even though the models differ, the outcomes have some common

features. In both cases, the alpine grasslands are identified as less suitable habitats,

while the forest areas adjoining open landscape formations in both models appear to

be highly suitable for the species.

Discussion

In this study, the habitat preference of bears within their home ranges was

analysed in two sites in the Pindos mountain range (north Greece). Habitat

selection is a scale dependent process and different characteristics of the landscape

Figure 3. The habitat suitability map produced by the ecological niche factor analysis for the

Grevena study site. Habitat suitability is presented by a greyscale gradient. The lighter the

colour the more suitable habitat location for the bear.



influence habitat selection at different scales (McLoughlin et al. 2002). At the

coarse scale, the entire extent of the study sites consists of suitable habitat for the

brown bear as demonstrated by the bear signs and telemetry data found

throughout the region. Both sites are mountainous regions with rich vegetation

coverage (mainly oak forests) and moderate human presence (small scale

agriculture, human population density well below the national average and roads

of low traffic load). However, this coarse scale analysis identifying the entire area

as suitable habitat does not assist decision making for specific projects within the

study sites as it lacks appropriate refinement. In order to assist the environmental

management of the two sites, the preferences of the bears within their home ranges

had to be analysed, and areas of higher and lower suitability within a region that is

suitable had to be identified.

In the present analysis, location data from bear signs were treated in the same

way as telemetry data as in the Clevenger et al. study (1997). It was possible to treat

the bear signs data in this way since the sampling design covered the entire study area

and sampling effort was distributed in proportion to habitat type. The telemetry data

offered a detailed insight into the behaviour and habitat use of only a limited number

of individuals; and thus were spatially limited within the home ranges of these

individuals. The bear signs, on the other hand, covered the entire study site and

originated from a large number of individuals, but were biased towards the road

network. Therefore, the combination of the two data sets was the most appropriate

way to counterbalance the limitations of each data set. Radiolocation data were of

two categories, VHF collars in Gramos and satellite GPS collars in Grevena. The

two methods had significant differences. The VHF collars allowed for lower

accuracy of estimation of the exact location of the animal and considerably lower

frequency of observations, while simultaneously demanding much more manual

effort. As a result, even though the same number of individuals was tracked in both

sites for a comparable period of time, the Gramos data set consisted of significantly

fewer observations of lower accuracy, and its analysis was performed at a coarser

scale than in the case of Grevena. Furthermore, VHF telemetry demanded strenuous

field work that was possible only during day time, and night time records are absent.

However, the use of the VHF collars was obligatory, because during that period

(1997–2002), satellite collars were not available.

However, despite the limitations in the method of data acquisition, the individual

behaviour of bears (Couturier 1954) and the intrinsic differences of the two study sites,

the results for the two datasets are directly comparable. This supports the robustness

of the present model. In general, the brown bear regional population units studied in

Gramos and Grevena show habitat selection patterns appearing to be also related to

food availability. McLoughlin et al. (2002) recorded habitat selection by grizzly bears

that corresponded well with the spatial and temporal availability of food on the

landscape. In the present study, even though bears avoid the human presence, they

prefer to come close (approx. 1 km) to human settlements of the study sites in selected

locations, such as orchards and cultivated fields that are known to play an important

role as seasonal key food resources (Mertzanis 1992; Kanellopoulos et al. in press). In

such sites high trophic value plant species occur such as: wheat (Triticum sp.), corn

(Zea mays), wild apples, (Malus spp.), wild pears (Pyrus amygdaliformis and Pyrus

pyraster), berries (Rubus hirtus, Rubus canescens, Rubus idaeus), Sorbus spp. (Sorbus

torminalis, Sorbus domestica and more rarely Sorbus aucuparia), Rosa spp. (Rosa



canina, Rosa agrestis, Rosa pulverulenta and Rosa heckeliana), Prunus spp. (Prunus

domestica), chestnuts (Castanea sativa), and hazelnuts (Corylus avellana). Selective

habitat use in relation to forest associations when bears come close to human

settlements was also recorded for brown bears in Slovenia (Kobler and Adamic 2000).

In both study areas the bears were recorded to prefer open formations with rich grass

availability (grasslands in Gramos and fallow land in Grevena) and in both areas the

animals were observed to forage in these openings. Satellite telemetry data showed the

use of these forest clearings to take place mainly during the night, as it was observed

also in Alberta by Nielsen et al. (2004). Preference for woodlands with deciduous

forest species (i.e. oak forest formations), is reported for the bear populations of

central Italy as well (Possilico et al. 2004), underlining the trophic significance of

acorns and beech nuts in the bears’ diet. Preference of locations close to surface waters

such as small valleys and riparian vegetation is also a characteristic feature of bears’

habitat use in the two study areas and is also reported for other bear populations of

north America as well (Stratman et al. 2001).

The final product of the analysis was a map of habitat suitability for the brown

bears in the two sites. This spatially explicit information allowed identification of

zones of high conservation value in the Gramos site, proposed to become a protected

area. The results of this study may be used by decision makers on the need to

characterize zones of protection and human restrictions in the area. In the case of

Grevena, the output of this study found an immediate application in the design of

the Via Egnatia highway that is currently being constructed in the area. The detailed

map allowed identification of locations where the detrimental effects of the highway

are likely to be significant and mitigate them with appropriate constructions, such as

a ‘‘green bridges’’ and other specialized mitigation infrastructures, such as wildlife

underpasses and overpasses.

Acknowledgements

Telemetry research was possible in the framework of LIFE-Nature projects LIFE96NAT/GR/

03222 (1997–1999) and LIFE99NAT/GR/06498 (2000–2002) co-funded by the E.U.

(DGENV/D1/LIFE Unit) (beneficiary NGO ‘‘Arcturos’’) as well as in the framework of the

‘‘Monitoring project on impact evaluation of Egnatia highway construction (stretch 4.1) on

large mammals in the area of Grevena (2002–2005)’’. This project was co-funded by

EGNATIA ODOS SA, Hellenic Ministry of Environment, Planning & Public Works and the

EU (DG Regio). We thank the Forestry Services of Kastoria, Grevena and Kalambaka for

forestry data provision and the field team composed by D. Bousbouras, G. Giannatos,

K. Grivas, I. Isaak, G. Iliopoulos, Tr. Karahalios, Alex. Karamanlidis, M. Matthiopoulos,

H. Papaioannou, S. Pistofidis, Ath. Tragos, I. Tsaknakis, S. Riegler, A. Riegler, S. Tiliopoulos

and V. Vitaniotis for field data collection.

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