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Brown bear (Ursus arctos L.) habitat use patterns in two regions of northern Pindos, Greece - management implications Georgios 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 To link to this Article: DOI: 10.1080/00222930701835175 URL: http://dx.doi.org/10.1080/00222930701835175
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Journal of Natural History Vol. 42, Nos. 5–8, February 2008, 301–315
Brown bear (Ursus arctos L.) habitat use patterns in two regions of northern Pindos, Greece – management implications Georgios Mertzanisa*, Athanasios S. Kallimanisb, Nikolaos Kanellopoulosc, Stefanos P. Sgardelisb, Athanasios Tragosa and Ilias Aravidisd
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a NGO ‘‘Callisto’’, Thessaloniki, Greece; bDepartment of Ecology, Aristotle University of Thessaloniki, Thessaloniki, Greece; cForestry Service, Metsovo, Greece; dDevelopment Agency of Thessaloniki, Thessaloniki, Greece
Conservation of brown bear (Ursus arctos L.) depends upon protecting the species’ core habitat and mitigating the detrimental consequences of infrastructure. Therefore, a detailed knowledge of bear habitat suitability is needed. Two sites were systematically surveyed in Pindos (Gramos and Grevena) for bear signs. Additionally, in Gramos six individuals were monitored with conventional VHF telemetry; and in Grevena a further six individuals were monitored with GPS-Simplex collars. The aim was to analyse the habitat preference of bears within their home ranges, and construct habitat suitability maps, using Ecological Niche Factor Analysis. Despite individual behavioural differences of bears and intrinsic differences between the sites, the two regional population units exhibit similar habitat preferences. The bears prefer agricultural land and areas near the edge between forests and open landscape formations (grasslands, cultivated fields, fallow land). Bears use alpine meadows less than randomly expected. Our results were used in zoning proposals for Gramos and for improvement of mitigation measures 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: mertzanis@callisto.gr; athantra@yahoo.gr ISSN 0022-2933 print/ISSN 1464-5262 online # 2008 Taylor & Francis DOI: 10.1080/00222930701835175 http://www.informaworld.com
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302 G. Mertzanis et al. 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
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Figure 1. Map of brown bear distribution range in Greece and location of the study areas in Pindos Mountain range.
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 temperatures 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
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304 G. Mertzanis et al. 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 (RX900 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
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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, DNA was 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, partially forested 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 around each 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. For each 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
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306 G. Mertzanis et al. 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. Chisquare 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: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pi ð1{pi Þ pi ð1{pi Þ vpi vpi zz0:05=2k pi {z0:05=2k n n 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
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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 Gramos sample Nestoras Tsarnos Ondria Selinios Skotidas Zacharias Grevena sample Alexandros Rohamis Maya Felicia Aias Jeronymo
Sex
Age class
Telemetry period
Total radiolocations
male male female male male male
adult adult adult adult adult subadult
10.5 months 10.5 months 2.5 months 13 months 11 months 9 months
276 266 68 423 142 124
male male female female male male
adult adult adult adult adult adult
12 months 6 months 2 months 20 months 1 month 2 months
2060 696 164 1319 175 150
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308 G. Mertzanis et al. (.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) Landscape availability (%)
Dense forests Partially forested Grasslands Agricultural land Bare land Infrastructure
47.9 13.8 26.4 7.4 3.4 0.7
Bear presence (%) 47.2 9.8 30.8 5.7
95% CI for Landscape bear use availability (%)
(1063) 46.7–47.7 (220) 9.5–10.1 (693) 30.3–31.3 (127) 5.5–5.9
6.2 (139) 0.5 (11)
Grevena (X2, df55, p,0.001)
6.0–6.4 0.4–0.6
61.6 13.8 9.9 14.3 0.2 0.3
95% CI for Bear bear use presence (%) 65.0 12.9 8.8 13.4
(3881) 64.8–65.2 (769) 12.6–13.2 (525) 8.6–9.0 (799) 13.2–13.6
0.0 (0) 0.0 (0)
0.0–0.0 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.
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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) and a 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 open landscape 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
Oak (Quercus sp.) Black pine (Pinus nigra) Beech (Fagus sp.) White pine (Pinus leucodermis.) Mixed broadleaves Fir (Abies sp.) Open landscape formations
Gramos (X2, df55, p,0.001)
Grevena (X2, df55, p,0.001) Bear presence (%)
95% CI for bear use
Landscape availability (%)
Bear presence (%)
95% CI for bear use
Landscape availability (%)
26.4 16.7
20.3 (457) 19.2 (433)
20.0–20.7 18.8–19.6
36.3 29.7
16.4 1.0
17.2 (387) 0.1 (2)
16.8–17.6 0.07–0.13
3.6 3.6
2.4 (144) 0.3 (18)
2.3–2.5 0.2–0.4
0.6
0.2 (5)
0.1–0.3
1.5
3.7 (222)
3.5–3.9
0.6 38.0
0.0 (0) 43.0 (969)
0.0–0.0 42.5–43.5
0.7 24.6
38.5 (2302) 38.2–38.8 32.3 (1931) 32.0–32.6
0.5 (30) 0.4–0.6 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.
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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.
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
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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.
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
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312 G. Mertzanis et al. 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
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Journal of Natural History
313
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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