|Assessing the suitability of central European landscapes for the reintroduction of Eurasian lynx
STEPHANIE SCHADT*†, ELOY RE VILLA*‡, THORSTEN WIEGAND*, FELIX KNAUER§, PETR A K ACZ ENSKY¶, URS BR EITENMOSER**,
LUD E K BUF KA††, JAROSLAV C ERV EN Y ‡‡, PETR KOUBEK‡‡, THOMAS HUBER¶, CV ETKO STANI S A§§ and L U DWIG TR EPL†
*Department of Ecological Modelling, UFZ Centre for Environmental Research, Permoser Str. 15, D-04318 Leipzig, Germany; †Department für Ökologie, Lehrstuhl für Landschaftsökologie, Technische Universität München, Am Hochanger 6, D-85350 Freising-Weihenstephan, Germany; ‡Department of Applied Biology, Estación Biológica de Doñana, Consejo Superior de Investigaciones Científicas, Avenida María Luisa s/n, E-41013 Sevilla, Spain; §Wildlife Research and Management Unit, Faculty of Forest Sciences, Technische Universität Munich, Field Research Station Linderhof, Linderhof 2, D-82488 Ettal, Germany; ¶Institute of Wildlife Biology and Game Management, Agricultural University of Vienna, Peter-Jordan-Str. 76, A-1190 Vienna, Austria; **Institute of Veterinary Virology, University of Bern, Länggass-Str. 122, CH-3012 Bern, Switzerland; ††Íumava National Park Administration, Sußická 399, CZ-
34192 Kaßperské Hory, Czech Republic; ‡‡Institute of Vertebrate Biology, Academy of Sciences of the Czech
Republic, Kv´tná 8, CZ-60365 Brno, Czech Republic; §§State Forest Service, Slovenia, Rozna ul. 36, SLO-1330
1. After an absence of almost 100 years, the Eurasian lynx Lynx lynx is slowly recover- ing in Germany along the German–Czech border. Additionally, many reintroduction schemes have been discussed, albeit controversially, for various locations. We present a habitat suitability model for lynx in Germany as a basis for further management and conservation efforts aimed at recolonization and population development.
2. We developed a statistical habitat model using logistic regression to quantify the factors that describe lynx home ranges in a fragmented landscape. As no data were available for lynx distribution in Germany, we used data from the Swiss Jura Mountains for model development and validated the habitat model with telemetry data from the Czech Republic and Slovenia. We derived several variables describing land use and fragmentation, also introducing variables that described the connectivity of forested and non-forested semi-natural areas on a larger scale than the map resolution.
3. We obtained a model with only one significant variable that described the connec- tivity of forested and non-forested semi-natural areas on a scale of about 80 km2. This result is biologically meaningful, reflecting the absence of intensive human land use on the scale of an average female lynx home range. Model testing at a cut-off level of P > 0·5 correctly classified more than 80% of the Czech and Slovenian telemetry location data of resident lynx. Application of the model to Germany showed that the most suitable habitats for lynx were large-forested low mountain ranges and the large forests in east Germany.
4. Our approach illustrates how information on habitat fragmentation on a large scale can be linked with local data to the potential benefit of lynx conservation in central Europe. Spatially explicit models like ours can form the basis for further assessing the population viability of species of conservation concern in suitable patches.
Key-words: GIS, large-scale approach, logistic regression, Lynx lynx, spatially explicit connectivity index, species reintroduction, statistical habitat model.
Effective nature conservation and habitat restoration in human-dominated landscapes require an under- standing of how species respond to habitat fragmenta- tion. As anthropogenic activities such as agriculture or urban development become prevalent in a region, native habitats are reduced in area and exist ultimately as remnants in a highly altered matrix (Miller & Cale
2000). Large carnivores provide some of the clearest examples of the fate of species that have to cope with fragmented multi-use landscapes on a large scale. Cen- tral Europe was once covered by dense temperate deciduous forests. However, after more than 5000 years of intense human activities only 2% of the original prime forest remains. At the beginning of the 20th cen- tury, wolves Canis lupus, brown bears Ursus arctos and Eurasian lynx Lynx lynx were almost extinct. Since then, there has been slow recovery of wolves in Spain and Italy (Boitani 2000), and bears and Eurasian lynx in Scandinavia, the Carpathians and the Balkan Penin- sula (Breitenmoser et al. 2000; Swenson et al. 2000).
The management and conservation of large carni- vores is particularly difficult due to their large require- ments for space. Intensive human land use is responsible for habitat fragmentation, which results in direct and indirect conflicts with those carnivores that compete with humans for the remaining semi-natural space and resources (Noss et al. 1996; Woodroffe & Ginsberg
1998; Revilla, Palomares & Delibes 2001). Many such species come into direct conflict with people because of their predatory habits. For example, the diet of lynx is basically formed of valuable game such as roe deer Capreolus capreolus and chamois Rupicapra rupicapra, but also includes sheep and red deer Cervus elaphus (Breitenmoser & Haller 1993; Jedrzejewski et al. 1993; Okarma et al. 1997; Jobin, Molinari & Breitenmoser
2000; Cerveny et al. 2002; Stahl et al. 2001). The patchy distribution of suitable habitat and construction of lin- ear barriers such as highways can lead to higher mor- tality (Kaczensky et al. 1996; Mace et al. 1996; Clevenger, Chruszcz & Gunson 2001). Therefore, conservation strategies for large carnivores focus on the integration of the species into multi-use landscapes inevitably domi- nated by people (Schröder 1998; Linnell, Swenson & Andersen 2000; Linnell et al. 2001).
Basic questions about the management and conser- vation of large carnivores still remain unanswered, for example about minimum habitat requirements under the new landscape conditions, and about whether recovery is only a local-scale phenomenon or can be expected to a greater extent in areas with dense human populations. These complex large-scale issues require knowledge of the extent, spatial arrangement and connectivity of potentially suitable habitat. In densely populated central Europe, the case of the reinvading Eurasian lynx poses exactly these questions. Since 1970 several successful efforts have been made to reintroduce lynx in Switzerland, France, Slovenia and the Czech
Republic (Herrenschmidt & Leger 1987; Breitenmoser et al. 1993; Cerveny, Koubek & Andera 1996; Cop & Frkovic 1998). In Germany there has been much contro- versy over lynx reintroduction, but natural immigration has already occurred into the Bavarian Forest due to the expansion of a population reintroduced to the Czech Bohemian Forest (Cerveny & Bufka 1996) (Fig. 1).
Given this situation, a large-scale assessment of hab- itat suitability is a necessary prerequisite for the evalu- ation of current initiatives for lynx reintroduction and management actions. Although the suitability of some areas for lynx has been ardently and controversially discussed in Germany, no quantitative habitat model yet exists to support these discussions, particularly one that can describe to what extent the species is tolerant of large-scale fragmentation. Some studies have modelled spatial factors that determine the distribution of the Eurasian lynx, but restricted to local areas (Zimmermann
& Breitenmoser 2001) or using algorithms that do not apply to fragmented areas (Corsi, Sinibaldi & Boitani
1998). Schadt et al. (in press) developed a rule-based habitat model for lynx in Germany, but this model has not been validated with any field data.
We aimed to develop a home range suitability model for the lynx in central Europe based on current under- standing of its requirements. We wanted our model to quantify general predictors for lynx home ranges to contribute to the design of a Germany-wide conservation plan by (i) identifying the broad distribution of suitable patches; (ii) obtaining an estimate of possible lynx home ranges in Germany; and (iii) providing a basis for a spatially explicit population simulation model to assess recolonization success and population development.
Habitat models using presence–absence data and logis- tic regression are useful in formalizing the relationship between environmental conditions and species’ habitat requirements, and in quantifying the amount of poten- tial habitat (Morrison, Marcot & Mannan 1992; Boyce
& McDonald 1999); they have been widely applied for a variety of purposes and species (Buckland & Elston
1993; FitzGibbon 1993; Wilson et al. 1997; Mace et al.
1999; Mladenoff, Sickley & Wydeven 1999; Palma, Beja & Rodrigues 1999; Rodriguez & Andrén 1999; Bradbury et al. 2000; Gates & Donald 2000; Manel, Buckton & Ormerod 2000; Orrock et al. 2000; Suarez, Balbontin & Ferrer 2000). The principle of this method is to contrast used habitat units vs. unused units in order to determine habitat suitability with a set of explanatory variables (Hosmer & Lemeshow 1989; Tabachnick & Fidell 1996). The regression function can then be extrapolated and mapped over target areas, in our case Germany and its neighbouring forests. We generated a home range suitability model based on local radio-tracking data obtained from lynx in the French and Swiss Jura Mountains (local study area), a landscape similar in fragmentation and population
Fig. 1. Permanent lynx populations in central Europe, sporadic and undetermined lynx occurrence (modified after Breitenmoser et al. 2000) and reintroduction initiatives for lynx in Germany. The black rectangles show the places from where we obtained telemetry data for developing the habitat model. PF, Palatine Forest; BF, Black Forest; BBF, Bavarian / Bohemian Forest.
density to the German low mountain ranges. This model was then extrapolated to Germany (large-scale study area) and evaluated with independent radio- tracking data from the low mountain range along the German– Czech border and from the Dinaric Moun- tain Range of southern Slovenia.
To provide a range of comparable data for areas not inhabited by lynx, i.e. unused units or non-observations, we created random home ranges in the local study area that we assumed to be in the general region of probable lynx movement and that lynx were likely to have visited, but where they had not settled as permanent residents. We assumed the resident home range areas to represent more desirable habitat than the non-occupied area.
The basic units for our analysis were raster cells based on the total lynx home range area irrespective of the animal, to avoid pseudoreplication due to home range overlap. We did not use single lynx location data, although we also used the telemetry data to gain insight into preferred land-use types. As the accuracy of the telemetry location data was 1 km2, we defined this as the spatial grain or landscape resolution. In order to consider information that comprised forest fragmenta- tion on a larger scale than our grid cell, we introduced two spatially explicit connectivity indices that described scale-dependent landscape properties to capture the individual’s landscape perception over larger areas.
STUY AREAS AND LYNX TELEMETRY DATA
Local-scale data for model development
Model development was based on lynx radio-collared and tracked in the Swiss Jura Mountains. The Jura Mountains are a secondary limestone chain between Switzerland and France with altitudes ranging between
372 and 1679 m a.s.l. The highlands are 53% covered by deciduous forest on the slopes, with coniferous forests on the ridges. Human population density reaches about 120 inhabitants km–2, and the area is intensively used for recreation. Cultivated areas are typically pas- tures used for grazing cattle (Breitenmoser & Baettig
1992; Breitenmoser et al. 1993).
We used 3402 radio-location data points published by Breitenmoser et al. (1993) from 13 individuals tracked from 1988 to 1991, of which four were resident females and three were resident males. The rest were dispersing subadults. One resident female had a home range shift during her observation period, and for ana- lytical purposes we considered her home range as belonging to two different individuals (giving a total of five home ranges of female lynx). Following the meth- odology proposed by Breitenmoser et al. (1993), we removed outlier locations before estimating the home ranges of the resident lynx using minimum convex polygons (MCP). The average home range sizes were then 169 km2 for females (n = 5) and 263 km2 for males (n = 3). For our analysis we defined the ‘closer
Fig. 2. Swiss Jura Mountain chain: home ranges of resident lynx (polygons), random home ranges (circles) and locations of dispersing lynx (triangles) in the closer study area (CSA). Light grey are grid cells that contain more than 66·6% of extensively used land-use types, such as forest or heathland (classed as PExt cells); dark grey are cells of the applied model with P > 0·5 (see the results of the logistic regression).
study area’ (CSA) as the MCP enclosing all locations, including residents and dispersers, to create a general region of probable lynx movement, with a buffer of
2·5 km, defined by the average daily distance moved
Local-scale data for model validation
German–Czech data. The forest cover of the low moun- tain chain along the German– Czech border (highest elevation at 1457 m) ranges from more than 90% in the inner parts (Sumava Mountains on the Czech side and Inner Bavarian Forest on the German side) to below
50% in the outer regions (e.g. Sumava Foothills, Outer Bavarian Forest and Fichtelgebirge). Population dens- ity ranges from 20 to 100 inhabitants km–2 (Cerveny & Bufka 1996; Wölfl et al. 2001) (Fig. 1). From the Sumava National Park we used the data of 714 radio- locations from five lynx observed between 1997 and
1999 (Bufka et al. 2000), one of them being a resident female having most of the centre of her home range in the Bavarian Forest on the German side. Two others were resident males and two were dispersing subadults.
Slovenian data. We used 677 telemetry locations from two resident females and three resident males over the period 1994 – 96 (Stanis˘a 1998). The lynx were descend- ants of six lynx reintroduced in the region in 1973 (Cop
& Frkovic 1998). The study area is part of the Dinaric
Mountain Range, stretching from Slovenia in the north to Albania in the south (Fig. 1). Elevations range from
300 to 1200 m, forest cover averages 90%, and the dom- inating forest community is Abieti–Fagetum dinaricum. Human population density is low, averaging 22 inhab- itants km–2, and the main human activities of the region are forestry, timber extraction and hunting with small amounts of recreation.
Large-scale study area for model application
Germany comprises an area of about 358 000 km2 with an average population density of 230 inhabitants km–2, which drops to about 100 inhabitants km–2 in places such as the low mountain ranges (e.g. Black Forest, Palatine Forest and Thuringian Forest). Urbaniza- tion accounts for 5% of the total area, and 30% of the total area is forested. The forests are clustered in areas formerly unsuitable for human activity in the low moun- tain ranges and in areas with poor soils in the north- east. Of the total area 2·5% is protected by National Park status. Germany has a very dense traffic network consisting of 11 000 km of highways and more than
50 000 km of interstate or main roads. We included neighbouring forest areas in Poland, the Czech Repub- lic, France and Belgium in our large-scale study area. We excluded the Alps as the habitat requirements of lynx in alpine biomes differ from those in low mountain ranges where we obtained our data.
We used CORINE land use data (European Topic Center on Land Cover, Environment Satellite Data Center, Kiruna, Sweden), which classify the following land use types on a 250-m grid. The CORINE classifica- tion names are provided in parentheses when differ- ent. (i) Urban areas (artificial territories); (ii) agricultural land (strongly artificial vegetated areas); (iii) pasture (less artificial vegetated areas); (iv) forests; (v) non- wooded semi-natural areas, e.g. heathland; (vi) wet- lands; (vii) water surfaces. Information on roads was digitized from 1 : 250 000-scale road maps. Roads included highways, transeuropean roads and main roads. Other paved roads, unpaved roads, unimproved forest roads and trails were not considered. All data were georeferenced on a Transverse Mercator projec- tion (spheroid Bessel, x-shift 3 500 000).
We created a raster map of 1-km mesh size and clipped it with the land use and road maps of the CSA in Switzer- land. Each lynx home range was intersected with the raster map, and descriptive environmental variables were extracted for each cell. We created five non-used home ranges for females and three for males of the aver- age size observed in the CSA. The position of these non- used home ranges was randomly assigned within the CSA, but without considering the area of lynx home ranges and big lakes (Bieler See and Neuenburger See) to help ensure that non-resident home range areas were likely to have been visited by lynx. Point distances were
7334 (9150) m from the edge, to avoid lying outside the CSA, and 14 668 (18 300) m between points, to avoid home range overlap in the same sexes for females (males). These points were then buffered with a radius
of 7334 m (= area of 169 km2) for females and 9150 m
Breitenmoser et al. 1993; Schmidt, Jedrzejewski & Okarma 1997). Human activity may strongly affect the presence of large carnivores by direct elimination or by individual avoidance of areas used by humans (Mladenoff et al. 1995; Woodroffe & Ginsberg 1998; Revilla, Palomares & Delibes 2001; Palomares et al.
2001). Availability of prey may also be important. Unfortunately, uniform data on prey density do not exist, so we were not able to include this information in our model.
Initially we compiled a number of potential predictor variables to describe fragmentation of large forest areas and intensive human land use (Table 1). We included variables related to the presence of forest within each grid cell, such as the percentage of forest, PFor, the number of forest patches, NPFor, and the perimeter of forest patches, PeriFor. We included other land uses such as the percentages of arable land, PAgr, pastures, PPast, and other non-forested semi-natural areas, PNat (Table 1). We also included the total number of patches of any land use, NPTot, and the percentage of extensive human land use, PExt. The latter was defined as the combined percentage of forest areas, PFor, and other non-forested semi-natural land cover, PNat, when the percentage of both land uses per cell was ≥ 66·6% (Table 1). This ensured that we also included margin cells of extensively used areas. Human variables included the percentage of urban areas, PUrb, and the number of urban polygons per cell, NPUrb (Table 1). We also compiled the total length of transeuropean and major roads, R50, per cell.
We introduced two spatial indices, RA and RC, that describe the connectivity or fragmentation of extens- ively used areas on larger scales than map resolution
(= area of 263 km2) for males. The random home ranges
(Fig. 3). We defined the index RA
(x, y, r) as the pro-
of the different sexes overlapped in two cases (Fig. 2).