Protected areas shape the spatial distribution of a European lynx

Protected areas shape the spatial distribution of a European lynx

Biological Conservation 177 (2014) 210–217
Contents lists available at ScienceDirect
Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Protected areas shape the spatial distribution of a European lynx
population more than 20 years after reintroduction
Jörg Müller a,b,⇑, Manfred Wölfl c, Sybille Wölfl d, Dennis W.H. Müller a, Torsten Hothorn e,
Marco Heurich a
a
Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany
Chair for Terrestrial Ecology, Department of Ecology and Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
Bavarian Environmental Agency, Hans-Högn-Straße 12, 95030 Hof/Saale, Germany
d
Luchsprojekt Bayern, Trailling 1A, 93462 Lam, Germany
e
University of Zürich, Division of Biostatistics, Hirschengraben 84, CH-8001 Zürich, Switzerland
b
c
a r t i c l e
i n f o
Article history:
Received 17 February 2014
Received in revised form 16 June 2014
Accepted 7 July 2014
Keywords:
National parks
Large carnivore monitoring
SCALP categories
Roe deer vehicle collision index
a b s t r a c t
The Bohemian Forest harbours one of the largest lynx populations in Central Europe, which arose from
animals reintroduced in two adjacent national parks. Despite an increasing number of population modelling approaches, the differences between potential and realised lynx distributions urgently need to be
explored. We used lynx monitoring data from 2005 to 2010 from 530 municipalities in eastern Bavaria
and spatial estimates of roe deer densities to test the predictions that the probability of lynx occurrence
(confirmed or unconfirmed) increases with (1) decreasing distance to the national park area, (2) increasing forest cover, (3) increasing proportion of state-owned forests, (4) increasing roe deer density and (5)
decreasing human activity. Using a flexible additive boosting model, we identified the distance to the
national parks as the dominant factor, with positive effects on lynx probability only up to 70 km from
the centre of the two national parks. Moreover, forest cover and roe deer density were correlated with
increasing lynx occurrence. The probability of unconfirmed lynx occurrence increased with the proportion of state-owned forest within a municipality. The most probable mechanism behind the distance variable is illegal killing outside of the national parks. We concluded that despite the small size of protected
areas in Central Europe, they still provide important source areas for this large predator. Moreover, the
results supported conclusions of previous modelling approaches on the exchange among existing subpopulations in Central Europe, and indicated that lynx currently might not be able to colonise the next
suitable areas.
Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction
In most Central European countries, the three large carnivores
Eurasian lynx (Lynx lynx), brown bear (Ursus arctos) and wolf (Canis
lupus) were considered as competitors of humans and were extirpated during the 18th or 19th century (Breitenmoser, 1998;
Linnell et al., 2001b). In the 20th century, wolf populations recovered in parts of Europe, and populations of all three species recovered in parts of Scandinavia. Such a return of large carnivores to
cultural landscapes evokes controversies throughout the world
(Kellert et al., 1996; Treves and Karanth, 2003), ranging from the
fear of hunters for their game or farmers for their livestock to the
⇑ Corresponding author at: Bavarian Forest National Park, Freyunger Str. 2, 94481
Grafenau, Germany.
E-mail address: [email protected] (J. Müller).
http://dx.doi.org/10.1016/j.biocon.2014.07.007
0006-3207/Ó 2014 Elsevier Ltd. All rights reserved.
hope that ungulate populations will be reduced in production
forests (Leopold, 1936; Linnell et al., 2010). Managers of large protected areas often promote such a return of large carnivores, with
the aim of re-establishing more natural species communities with
a restored guild of large predators (Millspaugh et al., 2010).
Despite the emotional fascination surrounding such enigmatic
species, ecological research still produces surprises and new
insights into factors shaping the distribution of large carnivores
(Jedrzejewski et al., 2002; Mech, 2012; Melis et al., 2010, 2009;
Ripple and Beschta, 2012; Ripple et al., 2014). Eurasian lynx populations in Northern Europe have been well studied, and several factors have been assumed or identified as determinants of lynx
distribution. For example, the probability of lynx occurrence has
been shown to be higher in areas with more than 50% forest cover
(Mikusinski and Angelstam, 2004). A positive effect of roe deer
density on lynx occurrence has been postulated because roe deer
J. Müller et al. / Biological Conservation 177 (2014) 210–217
are by far the most preferred prey of lynx, but this hypothesis has
been tested only in a few cases owing to the lack of roe deer density data (Basille et al., 2009; Herfindal et al., 2005; Hetherington
and Gormann, 2007). The extinction of large carnivores in general
is correlated with increasing human density (Woodroffe, 2000),
and the abundance and distribution of large predators is positively
affected by protected areas worldwide (Wegge et al., 2009;
Woodroffe and Ginsberg, 1998). Strict core zones often act as
reservoirs for source populations, but surrounding landscapes
often act as population sinks (Woodroffe and Ginsberg, 1998), possibly driven by habitat degradations, reduction in prey availability,
or an increase in (illegal) persecution just outside or at a distance
to protected areas. However, one can assume that in state-owned
Central European forests, lynx – in most countries a legally
protected species – are well protected because state foresters regularly manage ungulates themselves and thus no poaching by nonprofessional hunters would be expected, and that lynx will help
them to reduce undesired high ungulate populations (Hothorn
and Müller, 2010). Specifically, the aim is to support rare but highly
palatable tree species (species of, e.g. Abies, Quercus, Acer) by natural regeneration or by planting (Hothorn and Müller, 2010; Côté
et al., 2004).
One of the major lynx populations in Central Europe, consisting
of an estimated 50 individuals, is currently found in the Bohemian
Forest ecosystem along the border between the Czech Republic and
Germany (Kaczensky et al., 2013). This population originated from
lynx reintroduced in the area in two adjacent national parks: 5–10
individuals in the Bavarian Forest National Park, Bavaria, Germany,
in the early 1970s, and 17 individuals in the area of the presentday Šumava National Park in the Czech Republic in the 1980s
(Wölfl et al., 2001). Today, the number of resident individuals in
the area of both national parks is regularly reported by camera
traps to be about 18. Recently, lynx were reintroduced a second
time in Germany in another low mountain range, the Harz
Mountains (Linnell et al., 2009).
Here, we analysed the effect of a spatial variable describing the
distance to the release area of the lynx and several potentially relevant habitat parameters on the current status of the Bohemian
Forest lynx population more than 20 years after lynx reintroduction. We simultaneously tested the predictions that the probability
of current lynx occurrence should increase with (1) decreasing distance to the protected areas where the species was re-introduced,
(2) increasing proportion of state-owned forests, (3) decreasing
human activity, (4) increasing forest cover and (5) increasing roe
deer density.
2. Materials and methods
2.1. Study area
Our study area comprises the eastern municipalities of the
federal state Bavaria in south-eastern Germany. Mean annual temperatures range from 4.2 to 8.6 °C, mean annual precipitation
ranges from 600 to 1800 mm, and elevations range from 300 to
1450 m above sea level. The area forms the western mountain
ridge of the basal complex, and the Bohemian Forest lies in the
southern-most part. A major natural barrier of this ecosystem is
the Danube River, which forms the western border in the southern
part of the Bohemian Forest complex (Fig. 1). The selected area
represents a landscape with a high proportion and connectivity
of forests. The German side of the eastern low-range mountain
ridge mirrors coarsely the other half of the mountain area on the
Czech side. The geological conditions on the German and the Czech
sides are similar. Forest dominance increases at higher elevations,
whereas agriculture and settlements increase at low elevations.
211
Similarly, the shape of the lynx distribution on the Czech side
roughly mirrors the distribution on the German side (Wölfl et al.,
2001). The two adjacent state-owned national parks Bavarian Forest (240 km2; Germany) and Šumava (690 km2; Czech Republic)
are located in this area. Because of their protected core zones
and large forest areas, they were selected for the reintroduction
of lynx in Bavaria and the Czech Republic. The next lynx populations in the west (Vosges in France), in the east (Carpathian
Mountains in eastern Czech Republic) and in the north (Harz
Mountains in northern Germany) are more than 300 km away. A
small lynx population (3 individuals) closer to the study area
(150 km away) has been recently established in the Limestone Alps
National Park, but the Danube valley lying in between, with its
broad river and high human infrastructure, is likely a severe barrier. Reports of lynx bridging the Danube River are very rare
(unpublished data).
We restricted our statistical analysis to the Bohemian Forest in
the eastern part of Bavaria to avoid an influence of strong natural
dispersal barriers, such as the Danube River, extended and intensively used agricultural landscapes, highways, or combinations
thereof. Observations to date indicate that lynx are able to disperse
along the full mountain ridge from the south to the north (see
Fig. 1). We used 530 administrative municipalities as observational
units (Melis et al., 2010).
2.2. Lynx data
Lynx data were collected by the Bavarian Environment Agency
from 2005 to 2010 and are based on chance observations. No spatially strongly biassed data from systematic telemetry or camera
traps collected within the national parks were used. To cover the
whole area with an equal sampling effort for over a decade, a network of trained conservationists, foresters and hunters with expertise with large carnivores collected data (Wölfl et al., 2010). In this
network, about 80 people distributed across the whole study area
were trained to identify tracks or carcasses of lynx and other large
carnivores. These people are also responsible for evaluation of
domestic livestock or game animals killed by lynx, which is the
basis for compensatory payments. Observations were documented
according to the categories of the Status and Conservation of the
Alpine Lynx Population (SCALP; Molinari-Jobin et al., 2012), and
continuous training of the network members guarantees a data
of high quality. Such surveys are coarse, but have been shown to
be particularly informative on landscape scales of several thousand
square kilometres (Karanth et al., 2011).
We classified the lynx records used in groups according to their
validation, i.e. in three SCALP categories: C1, hard fact data, e.g.
dead lynx; C2, confirmed records, e.g. tracks verified by an expert;
and C3, unconfirmed data, e.g. any kind of direct visual observation
(for details, see Molinari-Jobin et al., 2012). These data were then
assigned to two groups as a potential index for lynx occurrence
in the municipalities. We assumed that, in general, a municipality
with either C1 or C2 records, i.e. confirmed records, is more likely
used by lynx than a municipality with only C3 records, i.e. unconfirmed records. We were aware that this is only an assumption that
cannot be proved by these data, but when the map was checked by
lynx experts, there was no spatial bias between occasional occurrences of lynx and more permanent occurrences. To avoid systematic errors owing to non-systematic sampling and different sizes of
municipalities, we used in our analysis only presence/absence data
of the two groups C1 + C2 and C3 as the most robust dependent
variable, instead of total count of records per municipality. Note
that a municipality that contains C1 + C2 + C3 observations was
assigned to both groups. The two datasets were analysed separately. To check for variation in time, we also built a data set for
each year.
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J. Müller et al. / Biological Conservation 177 (2014) 210–217
Fig. 1. Map of municipalities considered in the analysis. Dark grey indicates municipalities with at least one C1 or C2 record according to SCALP criteria (see text for details);
medium grey indicates a municipality with at least one C3 record. When both types occurred, C1/C2 is displayed. Inset: study area within Bavaria, Germany (dark grey) and
the next lynx populations (triangles) in Central Europe (very small population to the west in the Vosges Mountains, to the south in the Limestone Alps, to the north in the Harz
Mountains and to the west in eastern Czech Republic).
2.3. Roe deer density
Data on roe deer density are generally not available for larger
areas (Morellet et al., 2007). We took advantage of a recently
developed roe deer density index at the spatial scale of the
municipalities for Bavaria based on 74,000 deer–vehicle collisions
recorded in 2006 and 2009. The index is defined as the expected
number of deer–vehicle collisions per kilometre road length in
one year, adjusted for road type. An innovative modelling
approach for the number of deer–vehicle collisions allowed us
to construct a non-linear environment–deer relationship and to
assess the spatial heterogeneity (Hothorn et al., 2012). The index
is based on a model approach comprising predictors from
land-use data (Corine land cover; http://www.corine.dfd.dlr.de),
climate data (wordclim; Hijmans et al., 2005), browsing data
(monitoring by the Bavarian Forest Administration, based on a
fixed regular grid) and space (for details, see Hothorn et al.,
2012). The index was calculated within the exact same time span
in which the lynx data originated. Roe deer densities in the future
are difficult to forecast because controversial trends are possible.
On one hand, a severe reduction of roe deer is regularly claimed
and partly realised by foresters to reduce browsing damage
(Hothorn and Müller, 2010). However, only one-third of the
landscape is covered by forest, and increasing temperature caused
by global warming may increase also roe deer densities owing to
more suitable habitat conditions (Melis et al., 2010). Therefore,
we used two pessimistic and two optimistic scenarios for the
development of roe deer density expressed as an increase or a
decrease of 20% or 40%.
2.4. Human activity, forest cover and distance to the national parks
To estimate human activity independently from the roe deer
index (based on land cover, climate, browsing and space), we
selected the night-time light series from the National Geophysical
Data Center (www.ngdc.gov) using data from Satellite Nr. 16 in
2008. This provides a measure of night light on a raster basis with
a resolution of 1 km2. Due to the nocturnal activity peak of lynx
and the fact that these data of human activity include all types of
human activity during the night (from house lights to cars), these
data are particularly useful for measuring lynx-relevant human
activity. For each municipality, we calculated the average light
value. Because the human population changed only little within
the short time span studied in this analysis, we are confident that
the value from 2008 reflects the human activity of the whole time
span.
J. Müller et al. / Biological Conservation 177 (2014) 210–217
The forest cover of the municipalities (in%) was taken from Corine data by summarizing the Corine land-cover types 311 (broadleaf forest), 312 (coniferous forest), 313 (mixed forest) and 324
(transitional woodland-scrub) for each municipality with a resolution of 100 100 m (www.corine.dfd.dlr.de). Note that such a variable of the whole forest cover was not part of our roe deer index
model. The proportion of state-owned forests within the forest
area was taken from federal maps provided by the Bavarian Forest
Research Institute. To estimate the influence of area of the two
adjacent national parks (Bavarian Forest and Šumava), we calculated the centre of the two parks combined together and measured
the distance from this park centre to the centre of each municipality. Note that the centre of the combined national parks lies outside
Germany owing to the larger size of the Šumava National Park
(Fig. 1).
2.5. Statistical analysis
To model both types of lynx occurrence (group 1: C1 + C2 and
group 2: only C3 records), we applied a logistic additive boosting
model, which was fitted by a component-wise boosting algorithm
(Hothorn et al., 2011) using the function gamboost in the add-on
package mboost in R 2.3.0.2 (www.r-project.org). This model is particularly suitable for detecting non-linear functions and for dealing
with co-linearity of predictors (Hothorn et al., 2011). However, the
bivariate correlation of our final predictor variables did not exceed
|rspearman| = 0.50. Function cvrisk was used for cross-validated
estimation of the number of boosting iterations. In essence, boosting is a novel estimation technique for generalised additive models
including variable selections (for details, see Hothorn et al., 2011).
The boosting algorithm applied here fits the model by iteratively
minimizing the binomial log-likelihood by means of non-linear
functions of the environmental variables. Model inference was performed with the aim to select relevant variables. Model performance and variability were assessed by bootstrapping. The R
code and data are given in Appendix A. To investigate the stability
of lynx distribution between 2005 and 2010 within the study area,
we also modelled yearly lynx presence–absence scenarios for both
categories in a spatio-temporal model using an interaction term for
‘‘year’’ ‘‘distance to the national park’’. This model allows an estimation of a time (year)-varying effect of distance to the national
park.
The advantage of such a model framework is that also nonlinear additive effects, as to be expected for the distance to the
national parks, can be modelled. As we had a clear a priori assumption on the effect of state-owned forest proportions (probability of
lynx occurrence increases with increasing proportion of stateowned forests), we imposed a monotonicity restriction following
the method of Hofner et al. (2011).
3. Results
Within the 530-municipality study area, we obtained 1588 lynx
records from 220 municipalities. These records were confirmed
(C1 + C2 records) in 62 municipalities and unconfirmed (C3
records) in 158 municipalities; in 50 of these municipalities, the
records were both confirmed and unconfirmed.
When only statistically relevant variables were selected, our
model of confirmed records (C1 + C2) revealed a strong negative
correlation of the probability of lynx occurrence with increasing
distance to the centre of the two national parks (Fig. 2) up to
70 km, which is twice the utmost distance of a national park border
from the centre (vertical grey line in Figs. 2 and 3). Beyond this
distance, the probability of a lynx occurrence was lower than the
mean. The proportion of forest cover had a weak effect on the
213
probability of lynx occurrence, with positive effects between 50%
and 90% forest cover. With an increasing roe deer density index,
the model indicated continuously increasing probabilities of lynx
occurrence. The out-of-bootstrap log-likelihood of the final model
was significantly lower than the log-likelihood of the constant
model (p < 0.0001); our conclusions are therefore valid in the sense
that the covariate effects discussed above lead by far to a better
out-of-sample prediction of lynx presence (C1 + C2) than by
chance.
With our model, plots for unconfirmed lynx presence (C3)
yielded a curve for the distance to national parks with a shape
similar to that of plots of confirmed records, i.e. with a decreasing
positive contribution with distance, but the effect was pronouncedly less negative at a distance around 200 km and was
much less sharp (Fig. 3). Forest cover between 40 and almost
100% had a clear positive effect, with an optimum of unconfirmed
lynx presence at 70–80%. State-owned forest at proportions >80%
had a weak positive effect on unconfirmed lynx presence. Unconfirmed lynx presence was also affected by roe deer density
(selected by boosting), but the pattern was not clear (high variation
of bootstraps). As with the model for confirmed records, the outof-bootstrap log-likelihood of the final model of unconfirmed
records was significantly lower than the log-likelihood of the
constant model (p < 0.0001). For both dependent variables,
(C1 + C2) and C3, only the time (year)-constant effect, but not the
time-varying effects, of distance to the national park was selected,
which indicated stability of the lynx distribution over the study
period. A variation of the roe deer index revealed a decrease in
the habitat area (municipality area weighted by the lynx probability) by up to 32% in the pessimistic scenario of reducing roe deer
density by 40% and a slight increase in the habitat area of 7% in
the optimistic scenario of increasing roe deer density by 40%
(Fig. 4).
4. Discussion
Our analysis of lynx monitoring data together with data on the
location of protected areas, forest cover, human activity, and prey
density yielded new information on the potential drivers for the
spatial distribution of a reintroduced lynx population in Central
Europe. The models of our data strongly supported the prediction
that the probability of lynx occurrence (confirmed and unconfirmed records) decreases with increasing distance from the source
area, in this case the two adjacent national parks. In line with previous models (Kramer-Schadt et al., 2004), our models confirmed
the positive effect of increasing forest cover and a weak positive
effect of high proportions of state-owned forests. Roe deer density
had a clear positive effect on the probability of lynx occurrence for
municipalities with confirmed lynx observations, and a scenario of
a severe reduction in roe deer (the main prey of lynx) would
decrease the area suitable for lynx by roughly 1/3.
Unfortunately, we had no access to such a comprehensive set of
variables describing spatial effects of the distance to the release
area, habitat, prey and human activity for the Czech side of this
low-range mountain population. Our study area in Germany is
one-half of a mountain ridge with similar conditions on both
halves. Based on general information about the population on the
Czech side, the lynx reported in the northern municipalities of
our study area did not simply come from established areas across
the border (Wölfl et al., 2001). Also the lynx management policies
on both sides of the border are similar. The lynx is fully protected
in both countries, but poaching occurs on both sides (Cerveny et al.,
2002). Therefore, we are convinced that our study of ‘‘half of the
full habitat’’ reflects the situation of the whole population well.
Nevertheless, we highly urge conservation agencies in both
countries to collect data in the same way and to compile the data
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J. Müller et al. / Biological Conservation 177 (2014) 210–217
Fig. 2. Estimated partial contributions of environmental variables (mean values per grid) to confirmed lynx records (C1 + C2 according to Molinari-Jobin et al., 2012), as
selected by a boosting procedure. Partial contribution is a measure of the individual impact of each variable on the log-odds ratio for the occurrence of a confirmed lynx record
in a municipality; values >0 showed a positive effect of the predictor on the occurrence and vice versa. The solid black curve corresponds to the effect of the model fitted to the
full data set; grey curves visualise the bootstrap variability of the effect (based on 25 bootstrap samples). Note that the range of the log-odds ratio is the same for all variables,
and thus their relative importance can be compared directly. The ticks on the x-axis represent the observations; the vertical grey line on the plot of the distance to the
national park is the outer border of the Bavarian Forest National Park. The inset shows the position of the study area in Germany.
Fig. 3. Estimated partial contributions of environmental variables to unconfirmed lynx records (C3 according to Molinari-Jobin et al., 2012), as selected by a boosting
procedure. Partial contribution is a measure of the individual impact of each variable on the log-odds ratio for the occurrence of an unconfirmed lynx record in a municipality;
values >0 showed a positive effect of the predictor on the occurrence and vice versa. The solid black curve corresponds to the effect of the model fitted to the full data set; grey
curves visualise the bootstrap variability of the effect (based on 25 bootstrap samples). Note that the range of the log-odds ratio is the same for all variables, and thus their
relative importance can be compared directly. The ticks on the x-axis represent the observations; the vertical grey line in the plot of the distance to the national park is the
outer border of the Bavarian Forest National Park.
J. Müller et al. / Biological Conservation 177 (2014) 210–217
2500
Lynx habitat area (km²)
2000
1500
1000
500
0
-40%
-20%
0
+20%
+40%
Roe deer index scenarios
Fig. 4. Extrapolation of lynx distribution probabilities in four scenarios of roe deer
density index, calculated as the probabilities for a confirmed occurrence of lynx
multiplied by the area of a municipality. The four scenarios included a decrease or
an increase in the roe deer density of 20% or 40%.
on the whole population to use as a solid basis for further management decisions.
Despite the value of landscape-wide monitoring data, their use
has also certain limitations. We are aware that our data do not
indicate either real lynx activity or presence over the full time span
of 6 years or lynx reproduction in a municipality. This raises the
question of how well the data represent the lynx distribution pattern. However, we do not have to assume a lack of observations
because of the network of expert observers throughout the area
(see Methods), and we did not find a time-varying effect of the spatial variable. By pruning down the information to the simplest
form, i.e. with a presence/absence matrix, a potential sampling bias
can be avoided robustly (Basille et al., 2009).
The intensity of human activity measured by light index had no
effect on the probability of lynx occurrence in our study, which is
in contrast to the results of a Norwegian study (Basille et al.,
2009) on a scale similar to that of ours. In that study, lynx were
usually found in areas with higher human densities and road
densities than generally present in the area, but they avoided both
the highest and the lowest human and road densities. On the other
hand, several telemetry studies in Central Europe have shown that
lynx individuals sometimes use also landscapes that are heavily
used by humans (Breitenmoser and Breitenmoser-Würsten,
2008; Sunde et al., 1998). One has to consider that our study region
lacks large cities and can thus be described as a Central European
area with a relatively low human density. On a larger scale,
Woodroffe (2000) presented clear evidence that the extinction of
large carnivores in the past was clearly correlated with increasing
human population densities or activity. However, using coarse data
from current trends of large carnivore distributions in North
America and Europe, Linnell et al. (2001b) rejected the hypothesis
of Woodroffe (2000) regarding the current situation of large
carnivores. Linnell et al. (2001b) explain the lack of a negative
correlation of large predators with human population density as
an effect of new protection laws, effective management strategies
and an increased acceptance of predator presence by the public. In
our regional study, it seems probable that not the general human
population density but rather illegal hunting by few individuals
is more relevant, especially when we consider the clear decrease
in lynx presence probability outside the protected areas as an indication for a source sink process (Figs. 2 and 3). For Norway, Basille
215
et al. (2013) demonstrated a clear avoidance of roads within territories during the hunting season, but could not find such an effect
for poached individuals. Based on our experience, we believe that
poaching in Bavaria happens as in Norway, i.e. randomly in all
types of habitats used for hunting and in many habitats with low
human activity (low light intensity).
Our results are consistent with the results of previous studies
that indicate that forests are the main habitat of lynx (Mikusinski
and Angelstam, 2004; Niedziakowska et al., 2006; Schadt et al.,
2002; Zimmermann and Breitenmoser, 2002). Mikusinski and
Angelstam (2004) found lynx in areas with >50% forest cover,
and the results of our study were well in line with this finding
(>40% for unconfirmed records and >60% for confirmed records).
Moreover, municipalities with 60–90% forest cover had a positive
effect on the number of confirmed lynx observations. The ecological cause may be manifold, ranging from forests as resting sites
(Podgórski et al., 2008; Sunde et al., 1998) to forests as an important hunting ground with sufficient cover for an ambush predator
like the lynx (Balme et al., 2007; Dickson and Beier, 2002;
Podgórski et al., 2008). Here, the preference of lynx for forest cannot be simply explained by higher prey availability, as indicated by
our simultaneous analysis of lynx presence and roe deer densities
and by the occurrence of particularly high roe deer densities in
less-forested habitats (Hothorn et al., 2012).
The major prey of lynx across Europe is roe deer (Jedrzejewski
et al., 1993; Jobin et al., 2000; Krofel et al., 2014). We found a clear
increase in lynx presence probability with roe deer presence only
for the model of confirmed records of lynx presence. Under our
assumption (only substantiated by general expert knowledge on
occasional and more permanent lynx in Bavaria) that C1/C2 municipalities tend to have more resident lynx, and C3 municipalities
tend to have more dispersing lynx, the data indicated that territories are selected in areas with higher roe deer densities. Because
reproducing individuals are crucial for the population, conservation activities should be clearly focused on territorial and reproducing individuals (Crooks et al., 2009; Sarrazin and Legendre,
2000). Moreover, we demonstrated that a severe decrease in roe
deer density may affect available lynx habitats. If one critically
inspects the current game management strategies in Germany, it
is highly unlikely that such a reduction in roe deer will be realised.
Nevertheless, the results indicate that future management of lynx
should also consider roe deer management. So far, studies investigating predators and ungulates in Europe have used only expert
‘‘guess’’ data (Ripple and Beschta, 2012). Our index of roe deer density, on the other hand, may be much better validated by the high
number of unbiased reports of killed animals on roads compared to
reports from hunters, and opens avenues for large-scale monitoring of lynx prey density (see also Hothorn et al., 2012).
A major finding of our study was the dominant effect of the variable ‘‘distance to the centre of the two adjacent national parks’’.
This was surprising because it has often been assumed that protected areas in Europe are too small to sustain lynx populations
within their boundaries (Linnell et al., 2001a). This assumption is
also supported by information on the home range of ten GPScollared lynx in the two parks: none of these lynx stayed within
the borders of the two national parks during the entire tracking
period (unpublished data).
We are aware that this spatial variable is only a surrogate for
several potential ecological mechanisms that cannot be directly
measured. The first mechanism could be a pronounced fidelity of
young lynx to their natal area, which is also the area in which lynx
were reintroduced. However, this is improbable because lynx were
released more than 20 years ago, which is long enough for dispersing individuals to colonise adjacent suitable habitats, and because
established lynx are strictly territorial and would force sexually
mature young lynx to search for a new suitable home range. The
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J. Müller et al. / Biological Conservation 177 (2014) 210–217
second mechanism could be the suitability of the large patch of
forest in the two national parks as lynx habitat. Also this is not
probable because there are many other large, adjacent forest areas,
and forest cover was included in the model and also additionally
selected. The third mechanism could be a greater safety of lynx
in both national parks, which protect the lynx from vehicle collisions or illegal killings and thereby create both a source area for
the lynx population up to 70 km from the centre and sink areas
in the more distant municipalities. Outside the protected areas,
the habitat connectivity is reduced by barriers, such as human settlements or highways (as assumed for Switzerland; Zimmermann
et al., 2005), but along the border, large uninterrupted forests are
present. Furthermore, the total number of reported vehicle collisions is low. In contrast, reports of 62 lynx illegally killed in the
Bohemian forests in both countries have been recorded since the
lynx reintroduction, but all were outside the two national parks
(Cerveny and Bufka, 1996). Even in Scandinavia, where lynx are
hunted legally (35% of deaths), poaching still accounts for most
deaths (38%; Andren et al., 2006). Similarly, for the small population of the Iberian lynx, poaching has been identified as a major
cause of mortality next to disease (López et al., 2014). Furthermore,
released lynx in Slovenia show a high potential for population
expansion (Cop and Frkovic, 1998). These data corroborate the
view that illegal killing outside the protected areas has a strong
sink effect.
A source area within a distance of only 70 km from the centre of
the parks seems low if one considers that male and female lynx
individuals in Scandinavia disperse mean distances of 148 km
(maximum 428 km) and 47 km, respectively (telemetry study of
Samelius et al., 2012). In contrast, in Switzerland, where the
landscape is more comparable to that of our study area owing to
a similar high human impact, the mean effective dispersal distance
in which new home ranges are formed ranges from 26 km for
high-density lynx populations to 63 km for low-density lynx populations (Zimmermann et al., 2005). Also in our study, single observations occurred >200 km distant from the two national parks
(Fig. 1), but the partial contribution of the distance to national
parks on the log-odds ratio for the occurrence of lynx in a municipality indicates only negative contributions to the probability.
Thus, even if a lynx can theoretically occur in all of the evaluated
municipalities, the probability of occurrence in suitable municipalities above 70 km such a distance is lower than within a mean
municipality. Here it is important to distinguish between the distance from the source area of an individual recorded by telemetry
and the occurrence probability in a municipality along the distance
curve from the source area.
To improve the understanding of the real causes for the distance
curve, the dispersal of young lynx has to be investigated via telemetry. The effective distance to which lynx occurrence can radiate
from a protected area points to the crucial conservation questions
of whether lynx populations can be connected in Central Europe,
e.g. the population in our study area connected with the population of the Harz Mountains (see inset Fig. 1; Kramer-Schadt et al.,
2005), and whether the populations can expand their range to
loosely connected mountain massifs, such as the Ore Mountains
(adjacent to our study area in the north-east) or the Thuringian
Forest (adjacent to our study area in the north). Spatially explicit
population simulation models with demographic scenarios have
led to conclusions that only the patches along the German–Czech
border seem to be interconnected with the Ore Mountains
(Schadt et al., 2002). Our results provided a more pessimistic view.
The 70 km from the centre of the parks – if considered as successful
dispersal – is much too short to reach even the next low-range
mountain area, i.e. the Fichtel Hills and neighbouring Ore Mountains. Thus, our results support the assumption that the Bohemian
lynx population is still isolated (Kramer-Schadt et al., 2005). Our
findings are also in line with the results of a recent genetic analysis
at the species’ western range in Europe (Ratkiewiczs et al., 2012);
the populations consist of strongly discrete groups possibly
because of the peripheral location of the populations, habitat fragmentation and the strict territorial structure of lynx populations.
Our results allow us to propose four important implications for
the conservation management of lynx. First, our results underline
that the population is probably neither able to effectively colonise
adjacent suitable areas nor to connect with existing populations in
the Harz Mountains, in the eastern Czech Republic or in the Limestone Alps. Second, even if the two neighbouring national parks are
too small to harbour a viable population within their boundaries,
their combined area of 930 km2 can still act as a source area for
a wider range. Third, in a more general implication for Central
Europe, a sufficient number of individuals should be released
simultaneously into different areas close enough to ensure gene
flow to establish connected populations of lynx. Fourth, considering the preference of lynx for areas with a high roe deer density,
roe deer management should be taken into account in lynx conservation activities.
Acknowledgements
We thank all those who provided lynx records, Karen A. Brune
for linguistic improvement of the manuscript, and Mathieu Basille
and three anonymous reviewers for very helpful comments on a
previous version of the manuscript.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.biocon.2014.07.
007.
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