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Recovery of large carnivores in Europe’s modern human-dominated landscapes

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The conservation of large carnivores is a formidable challenge for biodiversity conservation. Using a data set on the past and current status of brown bears (Ursus arctos), Eurasian lynx (Lynx lynx), gray wolves (Canis lupus), and wolverines (Gulo gulo) in European countries, we show that roughly one-third of mainland Europe hosts at least one large carnivore species, with stable or increasing abundance in most cases in 21st-century records. The reasons for this overall conservation success include protective legislation, supportive public opinion, and a variety of practices making coexistence between large carnivores and people possible. The European situation reveals that large carnivores and people can share the same landscape.
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DOI: 10.1126/science.1257553
, 1517 (2014);346 Science et al.Guillaume Chapron
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References (3551)
28 May 2014; accepted 21 November 2014
10.1126/science.1256620
CONSERVATION
Recovery of large carnivores
in Europes modern
human-dominated landscapes
Guillaume Chapron,
1
*Petra Kaczensky,
2
John D. C. Linnell,
3
Manuela von Arx,
4
Djuro Huber,
5
Henrik Andrén,
1
José Vicente López-Bao,
1,6
Michal Adamec,
7
Francisco Álvares,
8
Ole Anders,
9
Linas Balčiauskas,
10
Vaidas Balys,
11
Péter Bedő,
12
Ferdinand Bego,
13
Juan Carlos Blanco,
14
Urs Breitenmoser,
4,15
Henrik Brøseth,
3
Luděk Bufka,
16
Raimonda Bunikyte,
17
Paolo Ciucci,
18
Alexander Dutsov,
19
Thomas Engleder,
20
Christian Fuxjäger,
21
Claudio Groff,
22
Katja Holmala,
23
Bledi Hoxha,
24
Yorgos Iliopoulos,
25
Ovidiu Ionescu,
26,27
Jasna Jeremić,
28
Klemen Jerina,
29
Gesa Kluth,
30
Felix Knauer,
2
Ilpo Kojola,
31
Ivan Kos,
29
Miha Krofel,
29
Jakub Kubala,
32
Saša Kunovac,
33
Josip Kusak,
5
Miroslav Kutal,
34,35
Olof Liberg,
1
Aleksandra Majić,
29
Peep Männil,
36
Ralph Manz,
4
Eric Marboutin,
37
Francesca Marucco,
38
Dime Melovski,
39,40
Kujtim Mersini,
41
Yorgos Mertzanis,
25
Robert W. Mysłajek,
42
Sabina Nowak,
43
John Odden,
3
Janis Ozolins,
44
Guillermo Palomero,
45
Milan Paunović,
46
Jens Persson,
1
Hubert Potočnik,
29
Pierre-Yves Quenette,
47
Georg Rauer,
2
Ilka Reinhardt,
30
Robin Rigg,
12
Andreas Ryser,
4
Valeria Salvatori,
48
TomažSkrbinšek,
29
Aleksandar Stojanov,
39
Jon E. Swenson,
3,49
László Szemethy,
50
Aleksandër Trajçe,
24
Elena Tsingarska-Sedefcheva,
19
Martin Váňa,
35
Rauno Veeroja,
36
Petter Wabakken,
51
Manfred Wölfl,
52
Sybille Wölfl,
53
Fridolin Zimmermann,
4
Diana Zlatanova,
54
Luigi Boitani
18
The conservation of large carnivores is a formidable challenge for biodiversity conservation.
Using a data set on the past and current status of brown bears (Ursus arctos), Eurasian lynx
(Lynx lynx), gray wolves (Canis lupus), and wolverines (Gulo gulo) in European countries, we
show that roughly one-third of mainland Europe hosts at least one large carnivore species,
with stable or increasing abundance in most cases in 21st-century records. The reasons for
this overall conservation success include protective legislation, supportive public opinion, and
a variety of practices making coexistence between large carnivores and people possible. The
European situation reveals that large carnivores and people can share the same landscape.
Largecarnivoresareamongthemostcon-
troversial and challenging group of species
to conserve in our modern and crowded world.
There is a deeply rooted hostility to these spe-
cies in human history and culture, because of
perceptions of their negative impacts on human
livelihoods (1). Large carnivore abundance and dis-
tribution have historically been reduced (2), and
their present conservation has become intertwined
with broader emotional, political, and socioeconomic
issues that further complicate this challenge (3). In
addition, large carnivores live at low densities and
have large spatial requirements (4). Accordingly,
the conservation of viable large carnivore pop-
ulations needs to be planned and coordinated on
very wide scales, often spanning many intra- and
international borders [i.e., requiring transboun-
dary management (5)].
Themaindebatearoundlargecarnivoreconser-
vation is whether there is enough suitable space
left for viable and ecologically functional popula-
tions (6). As the two main drivers of the current
biodiversity crisishuman overpopulation and
overconsumptionshow no sign of reducing, an
intuitive forecast could be that large carnivores will
persist only in highly managed protected areas
(with regular translocations being made to achieve
artificial connectivity) or in some remote and un-
inhabited wilderness areas. This approach derives
conceptually from the North American wilderness
model that separates people and nature and that
has further been adopted in many Asian, African,
and neotropical countries (6)(keeping people and
predators apart,the separation model). The ulti-
mateexpressionofthisapproachliesinthesouthern
African propensity to fence protected areas (6).
The alternative model, allowing people and pre-
dators together(coexistence model), following a
landscape-scale conservation approach, has rarely
been given proper consideration, probably because
it has been deemed a priori to fail because of the
existing conflicts between large carnivores and hu-
mans. This dichotomy of large carnivore conserva-
tion models is analogous to the land-sharing versus
land-sparing debate, which is ongoing in a wider
biodiversity conservation context (7).
We compiled data about the status (i.e., cur-
rent and past occurrence and abundance) of large
carnivores [brown bears (Ursus arctos), Eurasian
lynx (Lynx lynx), gray wolves (Canis lupus), and
wolverines (Gulo gulo)] in Europe (8). We show
that the European continent (considering all con-
tinental European countries excluding Belarus,
Ukraine, and Russia) is succeeding in maintaining,
and to some extent restoring, viable large carnivore
populations on a continental scale (Fig. 1 and
fig. S1). All mainland European countries ex-
cept for Belgium, Denmark, the Netherlands, and
Luxembourg have a permanent and reproducing
occurrence of at least one species of large carnivore
(Fig. 1). The total area with a permanent presence
of at least one large carnivore species in Europe
covers 1,529,800 km
2
(roughly one-third of main-
land Europe), and the area of occasional presence
is expanding, as the presence of solitary dispersing
wolves has been confirmed in both Denmark and
Belgium in recent times.
Brown bears presently occur permanently in
22 countries (485,400 km
2
) and can be clustered
into 10 populations, most of which are native pop-
ulations (tables S1 to S3). Eurasian lynx presently
occur permanently in 23 countries (813,400 km
2
)
and can be clustered into 11 populations, five
of them being native populations (tables S5 to S7).
Wolves currently occur permanently in 28 coun-
tries (798,300 km
2
) and can be clustered into 10
populations, which are all native (tables S9 to S11).
Wolverines, however, are only found in the three
Fennoscandic countries, and they permanently
occur over a total of 247,900 km
2
in two popula-
tions (tables S13 to S15). Because of the limited bio-
geographic distribution of wolverines, Fennoscandia
is the only region containing all four large carni-
vore species in Europe (171,500 km
2
), and could
be considered as a large-carnivore hot spot together
with southeastern Europe (Dinaric, Carpathian,
and Balkan regions) and the Baltics (fig . S2). Three
large carnivore species overlap over 593,800 km
2
in Europe (fig. S2).
Overall, Europe hosts several large and stable
populations on the order of thousands of individ-
uals, many medium-sized and increasing popu-
lations that number in the hundreds of individuals,
and a few small and declining populations with a
few tens of individuals. Interestingly, none of the
medium or large populations are declining. Brown
bears are the most abundant large carnivore in
Europe, with an estimated total number around
17,000 individuals, and all population ranges have
SCIENCE sciencemag.org 19 DECEMBER 2014 VOL 346 ISSUE 6216 1517
RESEARCH |REPORTS
on December 18, 2014www.sciencemag.orgDownloaded from on December 18, 2014www.sciencemag.orgDownloaded from on December 18, 2014www.sciencemag.orgDownloaded from
been relatively stable or slightly expanding (table
S2). Wolves are the second most abundant species,
with an estimated total number larger than 12,000
individuals (table S10). Most populations have
been increasing or stable during recent years,
although the Sierra Morena population (Spain)
is on the brink of extinction, with only one pack
detected in 2010 (9). In recent years, the larger
Iberian population has an uncertain trend, although
it seems stable, and the Karelian population has
declined (9). The estimated total number of
Eurasian lynx is around 9000 individuals (table
S6), and most populations have generally been
stable in the past decade, although most of the
reintroduced populations appear to have stag-
nated at relatively small sizes, and the Vosges-
Palatinian and Balkan lynx populations have
declined (9). Finally, the estimated total num-
ber of wolverines is 1250 individuals, and both
populations are increasing (table S14). Details
on large carnivore monitoring methods are given
in tables S4, S8, S12, and S16 and (9).
All four large carnivore species are persisting
in human-dominated landscapes (fig. S3) and
largely outside protected areas. The mean TSD
human density in areas of permanent large car-
nivore presence is 19.0 T69.9 inhabitants/km
2
(range: 0 to 1651) for brown bears; 21.8 T73.8
inhabitants/km
2
(range:0to2603)forlynx;36.7T
95.5 inhabitants/km
2
(range: 0 to 3050) for wolves;
and 1.4 T5.7 inhabitants/km
2
(range: 0 to 115) for
wolverines (fig. S3). These figures suggest species-
specific sensitivities of large carnivores to humans,
with wolves being most successful in adapting to
human-dominated landscapes (fig. S3). Wolverines
are somewhat special, because their distribution
is constrained by climatic conditions, which re-
stricts them to northern and high-altitude areas,
which have low human population densities (10).
These figures permit cautious optimism for the
occurrence, abundance, and trends for large carni-
vores in Europe. The general picture emerging from
the current status of large carnivores in Europe is
that these species have shown the capacity to sur-
vive in human-dominated landscapes, representing
an often underappreciatedconservationsuccess
story. Having high numbers of large carnivores
in such landscapes is not exclusive to Europe [the
United States has abundant populations of black
bears (Ursus americanus)andmountainlions(Puma
concolor)];however,thelargestspecies,brownbears
and wolves, occur in Europe with much higher hu-
man densities. For example, Europe hosts twice a s
many wolves (>11,000) as the contiguous United
States [~5500 wolves (11)], despite being half the
size (4.3 million km
2
versus 8 million km
2
)and
more than twice as densely populated (97 inhab-
itants/km
2
versus 40 inhabitants/km
2
).
We believe that the alternative view to the co-
existence model (i.e., the separation model), which
argues that the largest predators can only survive
in protected areas or wilderness, is a consequence
of former policy goals to exterminate these spe-
cies (12). However, our results underline that if
the separation model had been applied in Europe,
there would hardly be any large carnivore popula-
tions at all, because most European protected
areas are too small to host even a few large car-
nivore reproductive units (13).
Whereas large carnivores do not permanently
occur in the areas of highest human density in
Europe,theyhaveshownanabilitytorecolonize
areas with moderate human densities if they are
allowed, and to persist in highly human-dominated
landscapes and in the proximity of urban areas
(14,15) in highly fragmented landscapes consist-
ing of forest-farmland mosaics or even agro-
ecosystems. Our results are not the first to reveal
that large carnivores can coexist with people (1618),
but they show that the land-sharing model for
large carnivores (coexistence model) can be suc-
cessful on a continental scale.
The reasons for the success of large carnivores
in Europe range from coordinated legislation
shared by many European countries (19,20)to
context-specific management practices and insti-
tutional arrangements. Since the end of World
War II, Europe has benefited from stable politi-
cal institutions ensuring proper law enforcement.
The post-communist transition in Eastern Euro-
pean countries was not generally associated with
institutional collapse, with the exception of some
Balkan countries. This stability created the con-
ditions for securing land tenure and associated
rights for activities such as forestry and hunting,
which are preconditions for the development of
sustainable practices. The rise of environmental
movements in the 1970s provided the motivation
for various pan-European legislative agreements
to emerge that served to promote biodiversity
conservation. For example, the Bern Convention,
administered by the Council of Europe, covers all
countries included in this report, and the Habitats
Directive covers all 20 European Union member
states with a permanent occurrence of large car-
nivores. Consequently, the four large carnivore spe-
cies examined here enjoy some degree of legal
protection in all European countries. Large car-
nivores have also benefited from the socioeconom-
ic changes over the past four decades that led to an
improvement in habitat quality. For example,
Europe again hosts large populations of wild un-
gulates (21), which can sustain large carnivore
populations. The impact of human land-use ac-
tivities has also been declining in many areas
because of a widespread exodus from rural areas
and the associated abandonment of agricultural
land (22). These broad patterns are further accom-
panied by a variety of local, cultural, or regulatory
practices making coexistence between large
carnivores and people possible (15,23). One
important prerequisite has been to maintain and
revive traditional livestock protection measures
1518 19 DECEMBER 2014 VOL 346 ISSUE 6216 sciencemag.org SCIENCE
1
Grimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, 73091 Riddarhyttan, Sweden.
2
Research Institute of Wildlife Ecology, University of
Veterinary Medicine, Vienna, Savoyenstrasse 1, 1160 Vienna, Austria.
3
Norwegian Institute for Nature Research, Post Office Box 5685 Sluppen, 7485 Trondheim, Norway.
4
KORA, Thunstrasse 31,
3074 Muri bei Bern, Switzerland.
5
Biology Department of the Faculty of Veterinary Medicine, University of Zagreb, Heinzelova 55, 10000 Zagreb, Croatia.
6
Research Unit of Biodiversity (UO/
CSIC/PA), Oviedo University, 33600 Mieres, Spain.
7
State Nature Conservancy of Slovak Republic, Tajovskeho 28B, 974 01 Banská Bystrica, Slovakia.
8
CIBIO/InBio, Centro de Investigação em
Biodiversidade e Recursos Genéticos, Universidade do Porto, 4485-661 Vairão, Portugal.
9
Harz Nationalpark, Lindenallee 35, 38855 Wernigerode, Germany.
10
Nature Research Centre, Akademijos
2, 08412 Vilnius, Lithuania.
11
Association for Nature Conservation WBaltijos vilkas,WVisoriu 6A-54, 08300 Vilnius, Lithuania.
12
Slovak Wildlife Society, Post Office Box 72, 03301 Liptovsky Hradok,
Slovakia.
13
Biology Department of the Faculty of Natural Sciences, University of Tirana, Boulevard Zog I, Tirana, Albania.
14
Wolf Project, Consultores en Biología de la Conservación, Calle Manuela
Malasana 24, 28004 Madrid, Spain.
15
Centre for Fish and Wildlife Health, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3012 Bern, Switzerland.
16
Department of Game Management
and Wildlife Biology, Czech University of Life Sciences in Prague, Kamýcká 129, 165 21 Prague, Czech Republic.
17
Ministry of Environment of the Republic of Lithuania, Jakšto 4/9, 01105 Vilnius,
Lithuania.
18
Department of Biology and Biotechnologies, University of Rome La Sapienza,Viale dellUniversità 32, 00185 Roma, Italy.
19
Balkani Wildlife Society, Boulevard Dragan Tzankov 8,
1164 Sofia, Bulgaria.
20
Lynx Project Austria Northwest, Linzerstrasse 14, 4170 Haslach/Mühl, Austria.
21
Nationalpark Kalkalpen, Nationalpark Zentrum Molln, Nationalpark Allee 1, 4591 Molln,
Austria.
22
Provincia Autonoma di Trento - Servizio Foreste e Fauna, Via Trener no. 3, 38100 Trento, Italy.
23
Finnish Game and Fisheries Research Institute, Viikinkaari 4, 00790 Helsinki, Finland.
24
Protection and Preservation of Natural Environment in Albania, Rruga Vangjush Furxhi 16/1/10, Tirana, Albania.
25
Callisto Wildlife and Nature Conservation Society, Mitropoleos 123, 54621
Thessaloniki, Greece.
26
Faculty of Silviculture and Forest Engineering, Department of Silviculture, Transilvania University, 1 Beethoven Lane, 500123 Brașov, Romania.
27
Forest Research Institute
(ICAS) Bulevardul Eroilor Number 128, Voluntari, Ilfov, 077190 Romania.
28
State Institute for Nature Protection, Trg Mažuranića 5, 10000 Zagreb, Croatia.
29
University of Ljubljana, Biotechnical
Faculty, Jamnikarjeva 101, 1000 Ljubljana, Slovenia.
30
LUPUS German Institute for Wolf Mnitoring and Research, Dorfstrasse 20, 02979 Spreewitz, Germany.
31
Finnish Game and Fisheries
Research Institute, Oulu Game and Fisheries Research, Tutkijantie 2E, 90570 Oulu, Finland.
32
Department of Forest Protection and Game Management, Faculty of Forestry, Technical University
of Zvolen, T.G. Masaryka 20, 960 53 Zvolen, Slovakia.
33
Faculty of Forestry, University of Sarajevo, Zagrebačka 20, 71000 Sarajevo, Bosnia and Herzegovina.
34
Department of Forest Protection
and Wildlife Management, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 3, 61300 Brno, Czech Republic.
35
Friends of the Earth Czech Republic, Olomouc
Branch, Dolní Náměstí 38, 77900 Olomouc, Czech Republic.
36
Estonian Environment Agency, Rõõmu tee 2, 51013 Tartu, Estonia.
37
Office National de la Chasse et de la Faune Sauvage, ZI
Mayencin, 5 Allée de Béthléem, 38610 Gières, France.
38
Centro Gestione e Conservazione Grandi Carnivori, Piazza Regina Elena 30, Valdieri 12010, Italy.
39
Macedonian Ecological Society,
Arhimedova 5, Skopje 1000, FYR Macedonia.
40
Department of Wildlife Sciences, Georg-August University, Büsgenweg 3, 37077 Göttingen, Germany.
41
National Veterinary Epidemiology Unit, Food
Safety and Veterinary Institute, Rruga Aleksandër Moisiu 10 Tirana, Albania.
42
Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Pawińskiego 5a, 02-106
Warszawa, Poland.
43
Association for Nature Wolf,Twardorzeczka 229, 34-324 Lipowa, Poland.
44
Latvian State Forest Research Institute Silava,Rīgas Iela 111, Salaspils, 2169 Latvia.
45
Fundación Oso Pardo, Calle San Luis 17, 4° A, 39010 Santander, Spain.
46
Natural History Museum, Njegoseva 51, 11000 Belgrade, Serbia.
47
ONCFS-CNERA PAD, Equipe Ours, Chef de Projet,
Impasse de la Chapelle, 31800 Villeneuve de Rivière, France.
48
Istituto di Ecologia Applicata, Via B. Eustachio 10, 00161 Rome, Italy.
49
Department of Ecology and Natural Resource Management,
Norwegian University of Life Sciences, Postbox 5003, 1432 Ås, Norway.
50
St. István Unversity Institute for Wildlife Conservation, Páter Károly 1, 2103 Gödöllő, Hungary.
51
Hedmark University
College, Evenstad, 2480 Koppang, Norway.
52
Bavarian Agency of Environment, Hans-Högn-Strasse 12, 95030 Hof/Saale, Germany.
53
Lynx Project Bavaria, Trailling 1a, 93462 Lam, Germany.
54
Department of Zoology and Anthropology, Faculty of Biology/Sofia University WSt. Kliment Ohridski,WBoulevard Dragan Tzankov 8, 1164 Sofia, Bulgaria.
*Corresponding author. E-mail: gchapron@carnivoreconservation.org or guillaume.chapron@slu.se These authors contributed equally to this work.
RESEARCH |REPORTS
(livestock-guarding dogs, night corrals, and shep-
herds), as well as to invest in new techniques (electric
fences) as an important nonlethal tool to mini-
mize large carnivore depredationonlivestock(24).
The most severe challenges for large carnivore
conservation are in countries where large car-
nivores have previously been extirpated, where
the adaptations for coexistence have been lost, or
where husbandry practices have evolved toward
new production schemes. In such contexts, the
return of large carnivores can trigger social con-
flicts. For example, poaching enjoys social accept-
ance in rural areas of Norway (25), limits the
recovery of wolves in Scandinavia (26), and erad-
icated a reintroduced bear population in Austria
(27). In these areas, the practical challenges and
economic impacts of carnivore conservation have
escalated into social conflicts, where the carni-
vores have become symbols of wider political
divisions between rural and urban populations
and between individuals and groups with funda-
mentally different value orientations and interests.
At present, there is a conjuncture between many
policy areas combined with a generally supportive
public opinion, so that the positive forces have
been prevailing. However, the underlying nega-
tive forces are still present and could reemerge as
a result of ecological, social, political, or econom-
ic changes. There is a need to monitor both the
ecological situation and sociopolitical climate to
ensure that the current trends are maintained.
The European experience offers hope for wild-
life conservation in human-dominated landscapes
and is relevant to other areas of the world. Al-
though developing countries may lack many of
the institutions and capacities that have enabled
large carnivore recovery in Europe, there are other
examples of large carnivores persisting and recov-
ering in human-dominated landscapes and even in
cities (17,28,29). Clearly, the presence of large
carnivores in human-dominated ecosystems is as-
sociated with modified ecological conditions that
deviate from conditions in areas with little hu-
man activity. However, the fact that such species
can persist in these novel ecosystems encourages
optimism for the conservation of larger and more
connected large carnivore populations.
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Proc. Natl. Acad. Sci. U.S.A. 109, 1536015365 (2012).
19. Council Directive 92/43/EEC of 21 May 1992 on the
conservation of natural habitats and of wild fauna and flora
(1992); http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=
CELEX:31992L0043.
20. Convention on the Conservation of European Wildlife and
Natural Heritage (1979); http://conventions.coe.int/Treaty/en/
Treaties/html/104.htm.
21. J. D. C. Linnell, F. E. Zachos, in Ungulate Management in
Europe: Problems and Practices, R. Putman, M. Apollonio,
R. Andersen, Eds. (Cambridge Univ. Press, Cambridge, 2011),
pp. 1253.
22. L. M. Navarro, H. M. Pereira, Ecosystems (N. Y.) 15, 900912
(2012).
23. E. J. Knott et al., Eur. J. Wildl. Res. 60,8597 (2014).
24. J. D. C. Linnell et al., in Carnivore Ecology and Conservation:
A Handbook of Techniques, L. Boitani, R. A. Powell, Eds.
(Oxford Univ. Press, Oxford, 2012), pp. 314332.
25. K. E. Gangaas, B. P. Kaltenborn, H. P. Andreassen, PLOS ONE
8, e68849 (2013).
26. O. Liberg et al., Proc. R. Soc. Ser. B 279, 910915 (2012).
27. L. Kruckenhauser, G. Rauer, B. Däubl, E. Haring, Conserv.
Genet. 10, 12231233 (2009).
28. C. Vynne et al., PLOS ONE 6, e28939 (2011).
29. A. Zimmermann et al., in Wild Rangelands, J. du Toit, R. Kock,
J. Deutsch, Eds. (Wiley, Oxford, 2010), pp. 129151.
ACKNO WLED GME NTS
Shape files of all maps are available in Dryad at this address:
http://doi.org/10.5061/dryad.986mp. This study was partly
funded by European Commission contract 070307/2012/629085/
SER/B3. P.K., G.C., J.D.C.L., M.v.A., D.H., H.A., J.V.L.-B., and L.B.
designed the study; G.C. and J.V.L.B. wrote the paper with help
from P.K., J.D.C.L., M.v.A., D.H., H.A., and L.B.; and all authors
contributed data.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/346/6216/1517/suppl/DC1
Materials and Methods
Figs. S1 to S3
Tables S1 to S16
References (30258)
17 June 2014; accepted 13 November 2014
10.1126/science.1257553
SCIENCE sciencemag.org 19 DECEMBER 2014 VOL 346 ISSUE 6216 1519
Fig. 1. Distribution of large carnivores in Europe in 2011. Brown bears (top left), Eurasian lynx
(top right), gray wolves (bottom left), and wolverines (bottom right). Dark blue cells indicate areas of
permanent occurrence, and light blue cells indicate areas of sporadic occurrence. Numbers refers to
population identifications in tables S1 to S16. Orange lines indicate boundaries between populations.
RESEARCH |REPORTS
www.sciencemag.org/content/346/6216/1517/suppl/DC1
Supplementary Materials for
Recovery of large carnivores in Europe’s modern human-dominated
landscapes
Guillaume Chapron,* Petra Kaczensky, John D. C. Linnell, Manuela von Arx, Djuro
Huber, Henrik Andrén, José Vicente pez-Bao, Michal Adamec, Francisco Álvares, Ole
Anders, Linas Balčiauskas, Vaidas Balys, Péter Bedő, Ferdinand Bego, Juan Carlos
Blanco, Urs Breitenmoser, Henrik Brøseth, Luděk Bufka, Raimonda Bunikyte, Paolo
Ciucci, Alexander Dutsov, Thomas Engleder, Christian Fuxjäger, Claudio Groff, Katja
Holmala, Bledi Hoxha, Yorgos Iliopoulos, Ovidiu Ionescu, Jasna Jeremić, Klemen Jerina,
Gesa Kluth, Felix Knauer, Ilpo Kojola, Ivan Kos, Miha Krofel, Jakub Kubala, Saša
Kunovac, Josip Kusak, Miroslav Kutal, Olof Liberg, Aleksandra Majić, Peep Männil,
Ralph Manz, Eric Marboutin, Francesca Marucco, Dime Melovski, Kujtim Mersini,
Yorgos Mertzanis, Robert W. Mysłajek, Sabina Nowak, John Odden, Janis Ozolins,
Guillermo Palomero, Milan Paunović, Jens Persson, Hubert Potočnik, Pierre-Yves
Quenette, Georg Rauer, Ilka Reinhardt, Robin Rigg, Andreas Ryser, Valeria Salvatori,
Tomaž Skrbinšek, Aleksandar Stojanov, Jon E. Swenson, Lász Szemethy, Aleksandër
Trajçe, Elena Tsingarska-Sedefcheva, Martin Váňa, Rauno Veeroja, Petter Wabakken,
Manfred Wölfl, Sybille Wölfl, Fridolin Zimmermann, Diana Zlatanova, Luigi Boitani
*Corresponding author. E-mail: gchapron@carnivoreconservation.org or guillaume.chapron@slu.se
Published 19 December 2014, Science 346, 1517 (2014)
DOI: 10.1126/science.1257553
This PDF file includes:
Materials and Methods
Figs. S1 to S3
Tables S1 to S16
References (30–258)
1
Supplementary Materials:
Materials and Methods: We collected standardized information on the status
(abundance and distribution range) of large carnivores along all countries comprising
Europe (excluding Russia, Belarus and Ukraine) through a detailed questionnaire sent to
members of the Large Carnivore Initiative for Europe (LCIE; a specialist group of the
IUCN's Species Survival Commission; www.lcie.org) and other knowledgeable experts
in 2012. LCIE members compiled the most up to date and accurate data from their
country. This was mainly built on accessing results from national and regional
governmental large carnivore monitoring activities and official statistics, but also
included results from ongoing research and conservation projects (e.g. LIFE projects).
Some additional material was compiled from a literature review considering recent
reports or publications and/or by further contacting experts or authorities via e-mail or
telephone. The compiled information was presented to the EU Commission in 2013 (9)
and is the basis for this study. We did not cover the very small countries (e.g.
Lichtenstein, Andorra). The designation of Kosovo in tables S1-S16 is without prejudice
to positions on status, and is in line with UNSCR 1244/99 and the ICJ Opinion on the
Kosovo declaration of independence.
Species distribution
Large carnivore distribution range was separated into two categories using the following
criteria: (i) permanent presence, where a cell was occupied by the species at least 50% of
the time over 3 years or more and/or where there was either confirmed reproduction or
the presence of resident adult females and, (ii) sporadic occurrence, areas of occasional
presence (e.g. dispersers) and/or no reproduction. For the latter category, we
acknowledge that the ability of monitoring programs to detect, for instance, dispersing
individuals will vary greatly depending on the effort invested. We compiled distribution
maps for the four large carnivores pooling all data available during the last 3–5 years on a
10 x 10 km grid basis using ArcGIS 10.0 (ESRI Inc., Redlands, CA, USA). We selected
this grid size because all these species usually have large spatial requirements and an
average home range of a lynx (e.g. (30-32)), wolf (e.g. (33, 34)), bear (e.g. (35, 36)) or
wolverine (e.g. (37, 38)) is likely to cover several grid cells in most of the European
contexts they occur. Maps were compiled at two different spatial levels, on a country
basis and on a European level. Overlapping cells of transboundary populations were
assigned to the country with the higher level of occupancy, e.g. if a transboundary cell
was defined to be of permanent presence by one country and of sporadic presence by the
other country, the cell was given the status of permanent presence.
2
Species abundance
We reported population estimates (estimated number of individuals) for all large
carnivore populations by compiling the most recent country census data. We calculated
distribution ranges, considering both permanent and sporadic occurrence, based on the
number of cells on a population level with population borders defined according to (39).
However, because population boundaries have not been formally fixed, assignment of
cells to one or the other population remains open to interpretation for sporadic occurrence
at contact zones. The results from this survey were compared to data on large carnivores
at their lowest extend from WWII to the 1970s (references in tables S1, S5, S9, S13) even
for populations that reached a lowest extend before WWII (such as brown bears in
Scandinavia). Because actual and historical estimates are derived from different
monitoring methods and are not immediately comparable, change in estimates is shown
by a rough multiplying factor in tables S2, S3, S6, S7, S10, S11, S14 and S15. For each
large carnivore species we estimated the range of human densities in its range by
converting the GEOSTAT 1 x 1 km human density map (40) to the same 10 x 10 km grid.
Estimating the number of large carnivores in a given area is always a difficult task even
within a limited area and Europe shows a wide diversity of approaches that have been
developed based on different ecological situations (e.g. the presence or absence of snow),
different social situations (e.g. the extent to which hunters take part in the activity),
different financial situations, and different behaviours of large carnivores. As a result, the
quality of the census data reported by the different countries for the different species and
the different populations varies dramatically. Overall, the small populations are subject to
more intensive and costly monitoring methods than the larger populations where
monitoring largely attempts to document presence or relative densities. The fact that all
of the countries that are members of the European Union have reporting requirements
under the Habitats Directive (19) motivates a fair degree of activity in most of the area
(countries) considered in this study, and in some other countries wildlife management
institutions are well developed. However, some of the countries in southern Europe have
relatively poorly developed programs. The result is that in some countries methodology is
based on systematic sampling using methods such as capture-recapture based on non-
invasive genetic analysis of scats and hairs, whereas in other countries carnivore numbers
may well be an educated guess. This high variability in the data implies that the
population estimates may not be directly comparable between countries, populations or
time periods. As a consequence, the estimates for a few large carnivore populations have
decreased recently because of an improvement of census methodology (e.g. wolves in
Slovenia and Bulgaria; bears in Eastern Balkan). Nevertheless, we are confident that our
database constitutes the best available and most complete large-scale assessment of large
carnivore population estimates in Europe that is possible at this point in time. Hereafter,
we summarize the most common methods used to monitor large carnivore in Europe by
3
species. The detailed country- and species-specific information is available in a report to
the European Commission (9).
Brown bear
In several countries, genetic methods that use non-invasively collected DNA (from scats
or hairs) (41, 42) are an important component of bear monitoring (e.g. Norway, Sweden,
Italy, Austria, France, Greece, Slovenia, FYR Macedonia) or are used to compliment or
confirm data obtained by other methods (e.g. counts at feeding sites, snow tracking,
counts of females with cubs and telemetry) (Croatia, Poland, Slovakia, Spain). In the
countries without genetics and telemetry, absolute estimates are based on much weaker
grounds. The small populations are generally subject to more intense and costly
monitoring methods to precisely estimate population size (43), although the most closely
monitored population is one of the largest, in Scandinavia (44). In hunted populations
harvest data is used to identify population trends. Monitoring methods are further detailed
for each country in table S4 with references in table S1.
Lynx
Lynx monitoring is usually based on a combination of various methods (45-50).
Monitoring in the Scandinavian population is based on snow-tracking, harvest data,
genetics (to separate close living females with cubs), and collection of livestock
depredation cases, supported by telemetry and camera-trapping. In Finland (Karelian
population), snow-tracking and telemetry are used. In Estonia, Latvia and Poland
estimates are based on snow-tracking, supported by analysis of harvest bag data in
Estonia and Latvia. In the Carpathians, monitoring and population estimates are based
mainly on hunting ground counts, snow-tracking and expert assessments. For the Alpine,
Jura and Vosges populations, camera-trapping (including capture-mark-recapture in
reference areas and density extrapolation) is combined with the collection of different
data sets validated using the criteria developed by the Status and Conservation of the
Alpine Lynx Population (SCALP) project (51, 52). The same is true for the Balkan
population and the Bavarian part of the Bohemian-Bavarian population. The basic
monitoring methods for the Dinaric population are snow-tracking (all three countries),
genetic sampling and guesstimates (Slovenia and Croatia). In the Bohemian and Harz
region a variety of the methods is used including collection of signs, genetics and camera-
trapping (53). Monitoring methods are further detailed for each country in table S8 with
references in table S5.
Wolf
Wolf monitoring in Scandinavia is based on intensive snow tracking complemented with
genetics and telemetry allowing for good estimates of annual number of reproductions,
the total number of individuals, and even information on the inbreeding coefficient of
4
individual pack members (54-56). In the Finnish part of the Karelian population,
monitoring is based on intensive snow tracking, telemetry and some genetic analysis. In
the Baltics, harvest data, snow tracking and damage statistics are used for monitoring.
The Central European Lowlands population is monitored by using snow tracking and
other sign surveys in combination with genetics, camera trapping (Poland & Germany)
and telemetry (Germany). In the Carpathian population monitoring is largely based on
harvest and damage statistics and the collection of wolf signs by various interest groups
including hunters and foresters, however the main method remains an interpretation of
assessments made by the various hunting grounds where the methodology is somewhat
unclear. The Dinaric-Balkan population spans the most national borders and thus is
subject to the most diverse monitoring ranging from interviews with local people and
expert assessments based on harvest data, damage reports, sign surveys, camera trapping,
telemetry and genetics. The Italian peninsula population is also monitored through a mix
of signs collected over varying time periods by various interest groups, damage reports
and expert assessment. The Alpine wolf population is intensively monitored by genetics,
confirmed damages, camera trapping, intensive snow tracking and sign surveys (57, 58).
The Northwestern Iberian and Sierra Morena populations are monitored by rendezvous
site mapping using sign surveys in combination with provoked howling censuses to
confirm reproduction (59, 60). Monitoring methods are further detailed for each country
in table S12 with references in table S9.
Wolverine
In both Sweden and Norway wolverines are surveyed annually in March–May by snow
tracking and identification of natal dens which represent reproductions (37).
Reproductions are registered based on observations of cub tracks, visual observation of
cubs or documentation of den sites. Den sites are documented based on characteristics
that can separate den sites from cache sites or day-beds. In both Norway and Sweden
many of the sites are revisited during summer after snow melt to collect further evidence
of reproduction. Norway also has an annual collection of scats based on snow-tracking
using snow-scooters, harvest data and depredation reports. This survey aims to cover the
entire wolverine range each year. Genetic methods are used to conduct capture-mark-
recapture estimates of population size. The survey in Finland is based on snow-tracking
and line-transects performed in winter which aim to estimate the total number of
individuals in the population. See also table S13 and S16.
5
Fig. S1. Distribution of large carnivores in Europe at their lowest extend during the
1950–1970s. Brown bears (top left), Eurasian lynx (top right), grey wolves (bottom left)
and wolverines (bottom right. Numbers refers to populations ID in tables S1-S16. Orange
lines indicate boundaries between populations, which have been designated for the
purpose of this analysis. Countries can have different criteria and time periods for the
definition of cells with large carnivore presence.
6
Fig. S2. Distribution of large carnivore hotspots in Europe in 2011. Both permanent
and sporadic occurences are used to show overlap between species distribution.
7
Fig. S3. Human population density in large carnivore permanent presence areas.
The red curve shows for each species the density distribution of human population
densities (people / km2) for 10 x 10 km cells permanently occupied by a large carnivore
species. We only used permanent cells (where the species breed). The black curve shows
the density distribution of human population densities across all Europe (excluding
United Kingdom).
8
Table S1. Brown bear population names, countries and literature references.
Populations have been designated for the purpose of this analysis according to (39).
Literature refers to actual abundance and distribution data, monitoring methods, historical
abundance and distribution data. Population data do not cover Belarus, Russia and
Ukraine.
Population
Country
References
1. Scandinavian
Norway
(61-64)
Sweden
(41, 42, 44, 62, 65-67)
2. Karelian
Finland
(68-73)
Norway
(62, 63)
3. Baltic
Estonia
(74-79)
Latvia
(80, 81)
4. Carpathian
Poland
(82-85)
Romania
(9, 67, 78, 86)
Serbia
(9)
Slovakia
(67, 78, 87-90)
5. East Balkan
Bulgaria
(9, 88, 91-94)
Serbia
(9)
FYR Macedonia
(9, 95)
6. Dinaric-
Pindus
Albania
(9, 95-101)
Bosnia-
Herzegovina (9, 102)
Croatia
(103-105)
Greece
(9, 72, 87, 106-110)
Kosovo
(9)
FYR Macedonia
(9, 111)
Montenegro
(9)
Serbia
(9, 112, 113)
Slovenia
(114-117)
7. Central
Apennines Italy (43, 118, 119)
8. Alps
Austria
(27, 120)
Italy
(72, 121, 122)
Slovenia
(116, 117)
Switzerland
(123)
9. Pyrenean
France
(87, 124-127)
Spain
(87, 128, 129)
10. Cantabrian
Spain
(130-132)
9
Table S2. Brown bear population sizes and change. Literature is cited in Table S1 and
further details are provided in (9). Recent estimates are for years 2010, 2011 or 2012 and
methodologies may vary between countries. Some numbers may contain double count of
border individuals. Past estimates refer to the lowest abundance during the 1950-1970s.
Change factor indicates the rough multiplying factor from past to recent estimates, e.g. >
3 means the estimate has more than tripled, < 0.3 means it has decreased by more than
one third, “Return” indicates the species came back naturally after extinction and
“Reintrod.” indicates the species came back through reintroduced animals. Countries may
be labeled as “Reintrod.” if their sub-population originates from reintroduced animals in a
neighboring country. “-“ indicates no data is available.
Population Country
Recent
estimate
(individuals)
Past estimate
(individuals) Change
factor
1. Scandinavian
Norway
105
15–41
> 3
Sweden
3,300
400–600
> 5
Total
3,400
410–640
> 5
2. Karelian
Finland
1,600–1,800
150
> 10
Norway
46
9–26
> 2
Total
1,700
160–180
> 10
3. Baltic
Estonia
700
100
> 5
Latvia
10–15
Almost extinct
Return
Total
710
100
> 5
4. Carpathian
Poland
80
10–14
> 5
Romania
6,000
860
> 5
Serbia
6
-
-
Slovakia
800–1,100
300
> 3
Total
7,200
-
-
5. East Balkan
Bulgaria
530–590
450
Stable
Serbia
50
-
-
FYR
Macedonia - - -
Total
600
-
-
6. Dinaric-
Pindus
Albania
180-200
-
-
Bosnia-
Herzegovina 550 400 > 1.3
Croatia
1,000
400
> 2
Greece
350–400
100
> 3
Kosovo
-
-
-
FYR
160–200
-
-
10
Macedonia
Montenegro
270
-
-
Serbia
50–70
-
-
Slovenia
396–480
190
> 2
Total
3,070
-
-
7. Central
Apennines Italy 37–52 40 Stable
8. Alps
Austria
5
Extinct
Return
Italy 33–36
(Trentino) + 12
(Friuli) 8–10
Reintrod.
(Trentino)
+ Return
(Friuli)
Slovenia
5–10
0–5
Return
Switzerland
0–2
Extinct
Reintrod.
Total
45–50
8–15
> 4
9. Pyrenean
France
22 (includes
Spanish bears)
70 (includes
Spanish bears) < 0.3
Spain
22–27 (include
French bears)
70 (include
French bears) < 0.3
Total
22–27
70
< 0.3
10. Cantabrian Spain
195–210 (28
females with
cubs of the
year)
60 > 3
Total
17,000
-
-
11
Table S3. Range in km2 occupied by brown bears and change. Literature is cited in
Table S1 and further details are provided in (9). Our definition of range is not in the sense
of the Article 17 of the 92/433/EEC Habitats Directive (no smoothing over gaps) but
rather is representing occurrence or occupied cells in the EEA 10 x 10 km grid.
Methodologies may vary between countries. Overlapping or border cells were only
counted once. Past estimates refer to the lowest extend during the 1950–1970s. Change
factor indicates the rough multiplying factor from past to recent estimates, e.g. > 3 means
the estimate has more than tripled, < 0.3 means it has decreased by more than one third.
“-“ indicates no data is available.
Population
Permanent
range
Sporadic
range
Total
range Past range
Change
factor
1. Scandinavian
169,100
298,600
467,700
95,600
> 4
2. Karelian
80,100
301,400
381,500
108,000
> 3
3. Baltic
20,800
29,600
50,400
12,400
> 4
4. Carpathian
99,200
23,400
122,600
72,600
> 1.5
5. East-Balkan
18,900
20,100
39,000
13,000
> 3
6. Dinaric-
Pindus 78,700 35,400 114,100 71,100 > 1.5
7. Central
Apennines 2,300 41,00 6,400
imprecise
data -
8. Alps
1,400
10,800
12,200
600
> 20
9. Pyrenean 7,900 5,000 12,900
imprecise
data -
10. Cantabrian
7,700
7,700
1,800
> 4
Total
485,400
726,200
1,211,600
-
-
12
Table S4. Brown bear population monitoring methods. Literature is cited in Table S1
and further details are provided in (9). All methods may not be used on the whole species
distribution area. Abbreviations: CMR: Capture-Mark-Recapture, FCOY: count of
Females with Cubs Of the Year.
Population
Country
Monitoring methods
1. Scandinavian
Norway
Genetic CMR, dead bears, damage reports
Sweden
Genetic CMR, telemetry, dead bears,
damage reports, bear observation index
provided by moose hunters, density
extrapolation
2. Karelian
Finland
Genetic CMR, FCOY
Norway
Genetic CMR, dead bears, damage reports
3. Baltic
Estonia
FCOY, snow tracking, observations
Latvia
Sum of hunting ground "counts "
4. Carpathian
Poland
Telemetry, questionnaires to state forest
divisions & national parks
Romania
Genetic CMR, telemetry, camera trapping,
snow tracking, FCOY, sum of hunting
ground "counts"
Serbia
Genetic CMR, camera trapping, density
extrapolation
Slovakia
Genetic CMR, telemetry, camera trapping,
snow tracking, sum of hunting ground
"counts", expert opinion
5. East Balkan
Bulgaria
Genetic CMR, bear tracks, sum of hunting
ground "counts", density extrapolation,
expert opinion
Serbia
Genetic CMR, camera trapping, density
extrapolation, expert opinion
FYR
Macedonia
Genetic CMR, camera trapping, bear
tracks, sum of hunting ground "counts”
6. Dinaric-
Pindus
Albania
Camera trapping, bear tracks, expert
opinion
Bosnia-
Herzegovina Sum of hunting ground "counts "
Croatia
Genetic CMR, sum of hunting ground
"counts", density extrapolation,
coordinated feeding site counts
Greece
Genetic CMR, FCOY, camera trapping
13
Kosovo
-
The Former
Yugoslav
Republic of
Macedonia
Camera trapping, bear tracks, sum of
hunting ground "counts"
Montenegro
-
Serbia
Genetic CMR, camera trapping, density
extrapolation, expert opinion
Slovenia
Genetic CMR, harvest data, coordinated
feeding site counts
7. Central
Apennines Italy
Genetic CMR
8. Alps
Austria
Genetics, bear signs (SCALP criteria C1 &
C2)
Italy
Genetic CMR, camera trapping
Slovenia
Genetic CMR, coordinated feeding site
counts
Switzerland
Genetics, telemetry, bear signs
9. Pyrenean France
Genetic CMR, FCOY, bear signs, camera
trapping
Spain
Genetic CMR, FCOY, bear signs, camera
trapping
10. Cantabrian
Spain
Genetic CMR, FCOY
14
Table S5. Lynx population names, countries and literature references. Populations
have been designated for the purpose of this analysis according to (39). Literature refers
to actual abundance and distribution data, monitoring methods, historical abundance and
distribution data. Population data do not cover Belarus, Russia and Ukraine.
Population
Country
References
1. Scandinavian
Norway
(133-136)
Sweden
(137, 138)
2. Karelian
Finland
(73, 139-141)
3. Baltic
Estonia
(74, 75, 142)
Latvia
(143, 144)
Lithuania
(9, 145, 146)
Poland
(134, 147-149)
4. Carpathian
Bulgaria
(9, 134, 150-153)
Czech
(154-158)
Hungary
(159, 160)
Poland
(134, 147-149)
Romania
(9, 134, 161)
Serbia
(162)
Slovakia
(9, 134, 156, 163)
5. Balkan
Albania
(9, 96, 98-101, 134, 164)
FYR
Macedonia (9, 134, 164, 165)
Serbia,
Kosovo,
Montenegro,
Greece
(9, 112, 134, 164, 166-171)
6. Dinaric
Croatia
(9, 172)
Bosnia-
Herzegovina (9, 173)
Slovenia
(134, 174)
7. Bohemian-
Bavarian
Austria
(175)
Czech
(155, 176, 177)
Germany
(176, 178, 179)
8. Alpine
Austria
(180, 181)
France
(182, 183)
Italy
(184)
Slovenia
(134, 174, 182)
Switzerland
(49-51, 185-187)
15
9. Jura
France
(188)
Switzerland
(49, 185)
10. Vosges-
Palatinian
France
(188)
Germany
(179)
11. Harz
Mountain Germany (179, 189)
16
Table S6. Lynx population sizes and change. Literature is cited in Table S5 and further
details are provided in (9). Recent estimates are for years 2010, 2011 or 2012 and
methodologies may vary between countries. Some numbers may contain double count of
border individuals. Past estimates refer to the lowest abundance during the 1950–1970s.
Change factor indicates the rough multiplying factor from past to recent estimates, e.g. >
3 means the estimate has more than tripled, < 0.3 means it has decreased by more than
one third, “Return” indicates the species came back naturally after extinction and
“Reintrod.” indicates the species came back through reintroduced animals. Countries may
be labeled as “Reintrod.” if their sub-population originates from reintroduced animals in a
neighboring country. “-“ indicates no data is available.
Population Country
Recent
estimate
(individuals)
Past estimate
(individuals) Change
factor
1. Scandinavian
Norway
384–408 (65–
69 family
groups) 150 > 2
Sweden
1,400–1,900
(277 family
groups 175 > 5
Total
1,800–2,300
350–450
> 5
2. Karelian
Finland
2,430–2,610
Almost extinct
Return
3. Baltic
Estonia
790
115
> 5
Latvia
600
Almost extinct
Return
Lithuania
40–60
21
> 2
Poland
96
50
> 1.5
Total
1,600
190
> 5
4. Carpathian
Bulgaria
11
Extinct
Return
Czech
11
0–4
> 5
Hungary
1–3
Extinct
Return
Poland
200
100
> 2
Romania
1,200–1,500
500
> 2
Serbia
50
-
-
Slovakia
300–400
300–500
Stable
Total
2,300–2,400
-
-
5. Balkan
Albania
5–10
40–50
< 0.2
FYR
Macedonia 23 120 < 0.2
Serbia,
Kosovo, 15–25 80 < 0.2
17
Montenegro
Total
40–50
280
< 0.2
6. Dinaric
Croatia
50
Extinct
Reintrod.
Bosnia-
Herzegovina 70 Extinct
Reintrod.
Slovenia
10–15
Extinct
Reintrod.
Total
120–130
Extinct
Reintrod.
7. Bohemian-
Bavarian
Austria
5–10
Extinct
Reintrod.
Czech
30–45
Extinct
Reintrod.
Germany
12
Extinct
Reintrod.
Total
50
Extinct
Reintrod.
8. Alpine
Austria
3–5
Extinct
Reintrod.
France
13
Extinct
Reintrod.
Italy
10–15
Extinct
Reintrod.
Slovenia
5–10
Extinct
Reintrod.
Switzerland
96–107
Extinct
Reintrod.
Total
130
Extinct
Reintrod.
9. Jura
France
76
Extinct
Reintrod.
Switzerland
28–36
Extinct
Reintrod.
Total
110
Extinct
Reintrod.
10. Vosges-
Palatinian
France
19
Extinct
Reintrod.
Germany
0
Extinct
Reintrod.
Total
19
Extinct
Reintrod.
11. Harz
Mountain Germany 10 Extinct Reintrod.
Total
9,000
18
Table S7. Range in km2 occupied by lynx and change. Literature is cited in Table S5
and further details are provided in (9). Our definition of range is not in the sense of the
Article 17 of the 92/433/EEC Habitats Directive (no smoothing over gaps) but rather is
representing occurrence or occupied cells in the EEA 10 x 10 km grid. Methodologies
may vary between countries. Overlapping or border cells were only counted once. Past
estimates refer to the lowest extend during the 1950–1970s. Change factor indicates the
rough multiplying factor from past to recent estimates, e.g. > 3 means the estimate has
more than tripled, < 0.3 means it has been divided by more than one third, “Return”
indicates the species came back naturally after extinction and “Reintrod.” indicates the
species came back through reintroduced animals. “-“ indicates no data is available.
Population
Permanent
range
Sporadic
range
Total
range
Past
range
Change
factor
1. Scandinavian
476,100
240,400
716,500
155,800
> 4
2. Karelian
92,000
253,800
345,800
17,700
> 10
3. Baltic
82,300
44,700
127,000
95,200
> 1.3
4. Carpathian
112,600
34,700
147,300
78,700
> 1.5
5. Balkan
4,500
14,100
18,600
18,600
Stable
6. Dinaric
20,200
9,800
30,000
0
Reintrod.
7. Bohemian-
Bavarian
5,600
10,100
15,700
0
Reintrod.
8. Alpine
9,300
15,000
24,300
0
Reintrod.
9. Jura
9,400
8,400
17,800
0
Reintrod.
10. Vosges-
Palatinian
1,400
4,600
5,600
0
Reintrod.
11. Harz
Mountain
300
2,100
2,400
0
Reintrod.
Total
813,400
632,800
1,446,200
366,000
>3
19
Table S8. Lynx population monitoring methods. Literature is cited in Table S5 and
further details are provided in (9). All methods may not be used on the whole species
distribution area. Abbreviations: CMR: Capture-Mark-Recapture, SCALP: Status and
Conservation of the Alpine Lynx Population (see (51, 52) and
http://www.kora.ch/en/proj/scalp )
Population
Country
Monitoring methods
1. Scandinavian Norway
Telemetry, camera trapping, snow
tracking, harvest data, damage reports
Sweden
Telemetry, camera trapping, snow
tracking, harvest data, damage reports
2. Karelian
Finland
Telemetry, snow tracking
3. Baltic
Estonia
Telemetry, snow tracking, observations
Latvia
Telemetry, harvest data, sum of hunting
ground "count", expert opinion
Lithuania
Snow tracking, sum of hunting ground
"count", expert opinion
Poland
Genetics, telemetry, snow tracking, lynx
signs, expert opinion
4. Carpathian
Bulgaria
Camera trapping, snow tracking,
questionnaires and follow up field
investigations to confirm presence
Czech
Genetics, telemetry, camera trapping
CMR, snow tracking, sum of hunting
ground "counts " through questionnaires
Hungary
Camera trapping, questionnaires and
follow up field investigations to confirm
presence, expert opinion
Poland
Genetics, telemetry, snow tracking,
confirmed presence signs, expert opinion
Romania
Genetics, telemetry, camera trapping,
snow tracking, sum of hunting ground
"counts"
Serbia
Camera trapping
Slovakia
Genetics, telemetry, camera trapping,
snow tracking, sum of hunting ground
"counts"
5. Balkan Albania
Camera trapping, snow tracking,
observations, questionnaires
FYR
Genetics, telemetry, camera trapping,
20
Macedonia
snow tracking, density extrapolation, lynx
signs (SCALP criteria C1 & C2)
Kosovo
Questionnaires
6. Dinaric
Croatia
Genetics, telemetry, camera trapping,
snow tracking, dead lynx
Bosnia-
Herzegovina
Snow tracking, dead lynx
Slovenia
Genetics, telemetry, lynx signs (SCALP
methodology)
7. Bohemian-
Bavarian
Austria
Camera trapping, lynx signs (SCALP
criteria C1 & C2 and selected C3)
Czech
Genetics, CMR camera trapping, snow
tracking, sum of hunting ground "counts "
through questionnaires every 2 years
Germany
Telemetry, CMR camera trapping, snow
tracking, lynx signs (SCALP criteria C1 &
C2)
8. Alpine
Austria
Telemetry, camera trapping, lynx signs
(SCALP criteria C1 & C2)
France
CMR camera trapping, lynx signs (SCALP
criteria C1 & C2 and selected C3)
Italy
Telemetry, camera trapping, lynx signs
(SCALP criteria C1 & C2)
Slovenia
Genetics, lynx signs, snow tracking, expert
opinion,
Switzerland
Genetics, telemetry, CMR camera
trapping, lynx signs (SCALP criteria C1 &
C2)
9. Jura
France
CMR camera trapping, lynx signs (SCALP
criteria C1 & C2 and selected C3)
Switzerland
Genetics, telemetry, CMR camera
trapping, lynx signs (SCALP criteria C1 &
C2)
10. Vosges-
Palatinian
France
CMR camera trapping, lynx signs (SCALP
criteria C1 & C2 and selected C3)
Germany
CMR camera trapping, lynx signs (SCALP
criteria C1 & C2)
11. Harz
Mountain Germany
Telemetry, camera trapping
21
Table S9. Wolf population names, countries and literature references. Populations
have been designated for the purpose of this analysis according to (39). Literature refers
to actual abundance and distribution data, monitoring methods, historical abundance and
distribution data.
Population
Country
References
1. Scandinavian
Sweden
(54-56, 190)
Norway
(54-56, 191)
2. Karelian
Finland
(55, 56, 73, 192-197)
3. Baltic
Estonia
(74, 75, 198, 199)
Latvia
(198, 200)
Lithuania
(146, 198, 201, 202)
Poland
(203, 204)
4. Central
European
Lowlands
Germany
(205, 206)
Poland (9, 203, 204)
5. Carpathian
Czech
(158, 207)
Hungary
(208-210)
Poland
(203, 204, 211)
Romania
(9, 197)
Slovakia
(197, 212-214)
6. Dinaric-
Balkan
Albania
(9, 96-101, 215)
Bosnia-
Herzegovina (9, 216, 217)
Bulgaria
(9, 216, 218-220)
Croatia
(216, 221-223)
Greece
(197, 224-229)
FYR
Macedonia (9, 216)
Serbia
(9, 112, 216, 230, 231)
Slovenia
(232-234)
7. Italian
peninsula Italy
(235-239)
8. Alpine
Austria
(9)
France
(57)
Italy
(58, 240)
Switzerland
(185, 241)
9. NW Iberian
Spain
(59, 60, 242-245)
Portugal
(246-249)
22
10. Sierra
Morena Spain
(59, 243-245)
23
Table S10. Wolf population sizes and change. Literature is cited in Table S9 and
further details are provided in (9). Recent estimates are for years 2010, 2011 or 2012 and
methodologies may vary between countries. Some numbers may contain double count of
border individuals. Past estimates refer to the lowest abundance during the 1950–1970s.
Change factor indicates the rough multiplying factor from past to recent estimates, e.g. >
3 means the estimate has more than tripled, < 0.3 means it has decreased by more than
one third, “Return” indicates the species came back naturally after extinction. “-
indicates no data is available.
Population Country
Recent
estimate
(individuals)
Past estimate
(individuals) Change
factor
1. Scandinavian
Sweden
230–300 (30
packs + 25
pairs, incl.
border wolves)
Extinct Return
Norway
30 (3 packs + 2
pairs, exclud.
border wolves) Extinct Return
Total
260–330
Extinct
Return
2. Karelian
Finland
150–165
Almost extinct
Return
3. Baltic
Estonia
200–260
-
-
Latvia
200–400
Almost extinct
Return
Lithuania
300
34–56
> 5
Poland
267–359 (67–
77 packs) 11 > 28
Total
870–1,400
-
-
4. Central
European
Lowlands
Germany
43 (14 packs +
3 pairs) Extinct Return
Poland
100–110 (22
packs + 2
pairs) Extinct Return
Total
150
Extinct
Return
5. Carpathian
Czech
1
Extinct
Return
Hungary
1–5
Extinct
Return
Poland
209–254 (47–
51 packs) 45 > 5
Romania
2,300–2,700
1550
> 1.5
Slovakia
200–400
100–150
> 2
Total
3,000
1700
> 1.5
24
6. Dinaric-
Balkan
Albania
200–250
-
-
Bosnia-
Herzegovina 650 1000 < 0.7
Bulgaria
700–800
100–150
> 5
Croatia
168–219 (50
packs) 50 > 5
Greece
700
500
> 1.4
FYR
Macedonia 466 267 > 1.5
Serbia
750–850
500–600
> 1.4
Slovenia
32–43
10–15
3
Total
3,900
-
-
7. Italian
peninsula Italy 600–800 100 > 5
8. Alpine
Austria
2–8
Extinct
Return
France
13 packs + 7
border ones Extinct Return
Italy
12 packs + 7
border ones Extinct Return
Switzerland
8
Extinct
Return
Total
160 (32 packs)
Extinct
Return
9. NW Iberian
Spain
2,000
350–500
> 4
Portugal
220–435
150–200
> 1.5
Total
2,200–2,500
500–700
> 3
10. Sierra
Morena Spain 6 (1 pack) 60 (10 packs) < 0.1
Total
12,000
-
-
25
Table S11. Range in km2 occupied by wolves and change. Literature is cited in Table
S9 and further details are provided in (9). Our definition of range is not in the sense of the
Article 17 of the 92/433/EEC Habitats Directive (no smoothing over gaps) but rather is
representing occurrence or occupied cells in the EEA 10 x 10 km grid. Methodologies
may vary between countries. Overlapping or border cells were only counted once. Past
estimates refer to the lowest extend during the 1950–1970s. Change factor indicates the
rough multiplying factor from past to recent estimates, e.g. > 3 means the estimate has
more than tripled, < 0.3 means it has decreased by more than one third, “Return”
indicates the species came back naturally after extinction. “-“ indicates no data is
available.
Population
Permanent
range
Sporadic
range
Total
range
Past
range
Change
factor
1. Scandinavian
55,600
170,500
226,100
0
Return
2. Karelian
25,300
112,400
137,700
41,100
> 3
3. Baltic
94,200
49,200
143,400
42,800
> 3
4. Central
European
Lowlands
15,700
8,400
24,100
0
Return
5. Carpathian
144,200
27,000
171,200
59,900
> 2
6. Dinaric-
Balkan
256,500
74,900
331,400
139,300
> 2
7. Italian
peninsula
55,000
2,400
57,400
9,900
> 5
8. Alpine
33,200
26,800
60,000
0
Return
9. NW Iberian
116,600
3,700
120,300
69,200
> 1.5
10. Sierra
Morena
800
0
800
12,900
< 0.1
Total
798,300
481,800
1,280,100
375,100
> 3
26
Table S12. Wolf population monitoring methods. Literature is cited in Table S9 and
further details are provided in (9). All methods may not be used on the whole species
distribution area. Abbreviations: CMR: Capture-Mark-Recapture.
Population
Country
Monitoring methods
1. Scandinavian Sweden
Genetics, telemetry, snow tracking, dead
wolves, damage reports
Norway
Genetics, telemetry, snow tracking, dead
wolves, damage reports
2. Karelian Finland
Genetics, telemetry, snow tracking,
howling
3. Baltic
Estonia
Genetics, snow tracking, howling
observations, wolf signs
Latvia
Harvest data, sum of hunting ground
"counts", expert opinion
Lithuania
Snow tracking, sum of hunting ground
"count", expert opinion
Poland (NE)
Genetics, telemetry, snow tracking,
howling, wolf signs
4. Central
European
Lowlands
Germany
Genetics, telemetry, camera trapping,
snow and sand tracking, wolf signs
Poland (W)
Genetics, telemetry, snow tracking,
howling, wolf signs
5. Carpathian
Czech
Snow tracking, wolf signs
Hungary
Questionnaires and follow up field
investigations to confirm presence, expert
opinion
Poland
Genetics, telemetry, snow tracking,
howling, wolf signs
Romania
Genetics, telemetry, camera trapping,
snow tracking, howling, wolf signs, sum
of hunting ground "counts"
Slovakia
Genetics, camera trapping, snow tracking,
sum of hunting ground "counts"
6. Dinaric-
Balkan
Albania
Camera trapping, snow tracking, wolf
signs, questionnaires, expert opinion
Bosnia-
Herzegovina
Snow tracking, wolf signs, damage
reports, harvest data, expert opinion
Bulgaria
Genetics, telemetry, snow tracking
Croatia
Genetics, telemetry, camera trapping,
27
snow tracking, howling, wolf signs,
damage reports, expert opinion
Greece
Genetics, telemetry, camera trapping,
snow tracking, howling, wolf signs,
damage reports, questionnaires, expert
opinion
The Former
Yugoslav
Republic of
Macedonia
Sum of hunting ground "counts", expert
opinion
Serbia
-
Slovenia
Genetic CMR, telemetry, snow tracking,
howling
7. Italian
peninsula Italy
Genetics, telemetry, snow tracking,
howling, density extrapolation, expert
opinion
8. Alpine
France
Genetic CMR, snow tracking, howling,
wolf signs
Italy
Genetic CMR, snow tracking, howling,
wolf signs
Switzerland
Genetics, camera trapping, wolf signs
9. NW Iberian
Spain
Genetics, howling, wolf signs
Portugal
Howling, wolf signs
10. Sierra
Morena Spain Howling, wolf signs, damage reports
28
Table S13. Wolverine population names, countries and literature references.
Populations have been designated for the purpose of this analysis according to (39).
Literature refers to actual abundance and distribution data, monitoring methods, historical
abundance and distribution data.
Population
Country
Reference
1. Scandinavian
Norway
(250-254)
Sweden
(251, 252, 255, 256)
2. Karelian
Finland
(73, 251, 253, 257, 258)
Table S14. Wolverine population size and change. Literature is cited in Table S13 and
further details are provided in (9). Recent estimates are for years 2010, 2011 or 2012 and
methodologies may vary between countries. Some numbers may contain double count of
border individuals. Historical estimates refer to the lowest abundance during the 1950–
1970s. Change factor indicates the rough multiplying factor from past to recent estimates,
e.g. > 3 means the estimate has more than tripled.
Population Country
Recent
estimate
(individuals)
Past estimate
(individuals) Change
factor
1. Scandinavian
Norway
339–431 (58
reproductions) 100–150 > 3
Sweden
580–780 (118
reproductions) 60–100 > 5
Total
919–1211
160–250
> 5
2. Karelian
Finland
165–175
20–30
> 5
Total
10841386
350530
> 2
29
Table S15. Range in km2 occupied by wolverines and change. Literature is cited in
Table S13 and further details are provided in (9). Our definition of range is not in the
sense of the Article 17 of the 92/433/EEC Habitats Directive (no smoothing over gaps)
but rather is representing occurrence or occupied cells in the EEA 10 x 10 km grid.
Methodologies may vary between countries. Overlapping or border cells were only
counted once. Past estimates refer to the lowest extend during the 1950–1970s. Change
factor indicates the rough multiplying factor from past to recent estimates, e.g. > 3 means
the estimate has more than tripled.
Population
Permanent
range
Sporadic
range
Total
range
Past
range
Change
factor
1. Scandinavian
220,200
163,500
283,700
93,900
> 3
2. Karelian
27,700
43,900
71,600
18,300
> 3
Total
247,900
207,400
355,300
112,200
> 3
Table S16. Wolverine population monitoring methods. Literature is cited in Table S15
and further details are provided in (9). All methods may not be used on the whole species
distribution area. Abbreviations: CMR: Capture-Mark-Recapture.
Population
Country
Monitoring methods
1. Scandinavian Norway
Snow tracking for natal den mapping, genetic
CMR, dead wolverines, damage reports.
Sweden
Snow tracking for natal den mapping, dead
wolverines
2. Karelian
Finland
Snow tracking
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... Conservation measures and legal protection of the wolf began worldwide in the second half of the 20th century [8]. In North America, the US Endangered Species Act of 1973 included the wolf in its list of endangered species [5]. ...
... In Europe, large carnivores, including the wolf, have been protected by the 1979 Convention on the Conservation of European Wildlife and Natural Habitats, and the Nature 2000 network [9,10]. Other conservation measures, such as the implementation of protective laws, shared coordinated legislation by European countries, and socio-economic changes were also established [7,8]. These measures led to an increase in population numbers in the northern hemisphere, and therefore the number of attacks on domestic animals also increased [11]. ...
... The anonymity of the surveyed individuals was maintained. The questionnaire consisted of a total of 20 questions: those regarding tolerance towards wolves (1,2,3,4,15,16,17), questions regarding compensation programmes (5,6,7,8,9,10,11,12,13,14), and questions addressed only to ranchers regarding attacks on livestock (18,19,20). The questions were structured in 16 one forced-choice questions, a four-answer question to order according to the degree of importance, and three multiple-choice questions only aimed at ranchers. ...
Article
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Surveys have been used to study the current perception towards wolves by different stakeholders such as ranchers, landowners, hunters, experts in the field, and employees of the environmental administration in the provinces of Pontevedra and A Coruña, in the northwest of Spain. The main objective of this study is the evaluation and further discussion of the compensation offered to affected people for damages caused by wolf attacks and whether such compensations represent an improvement in the degree of tolerance towards these animals. Significant differences (p < 0.05) were found among the different sectors interviewed, with the hunters being the least tolerant sector, followed by ranchers. The number of attacks in the area was proven to influence their perspective toward wolves and the need for preventive measures. There was unanimity among hunters, ranchers, and locals, who do not consider the tools provided by the Galician administration sufficient to palliate the damages produced by wolves. However, 53.8% of ranchers, the group whose livelihood will most likely be affected by wolf attacks, and 60% of the wolf experts believe that compensation does not help to reduce tolerance towards wolves. Losing an animal makes people more likely to agree to the use of lethal and non-lethal methods.
... Seit über zwei Jahrzehnten kehren große Beutegreifer wie Luchs (Lynx lynx), Bär (Ursus arctos) und Wolf (Canis lupus) in viele europäische Länder zurückhauptsächlich aufgrund von sozial-politischen Veränderungen und durch das Inkrafttreten der Europäischen Naturschutzgesetzgebung zu Beginn der 1990er-Jahre, aber auch durch verbesserte Lebensraumbedingungen und einen aus gezeichneten Beutetierbestand. Daran entfachen sich seitdem immer wieder Mensch-Wildtier-Konflikte (Chapron et al. 2014;Linnell et al. 2017), und die wachsende Wolfspopulation in Deutschland bildet dabei keine Ausnahme. Während laut Umfragen die Mehrheit der deutschen Öffentlichkeit Wölfe wertschätzt (NABU 2015(NABU , 2018(NABU , 2021, wird dies von anderen Interessengruppen nicht unbedingt geteilt. ...
... Oft stehen hier dann Sorgen und Probleme rund um ökonomische und materielle Auswirkungen, wie etwa durch Nutztierrisse oder Konkurrenz um Jagdwild, im Vordergrund. Ängste, Haus-und Nutztiere oder die eigene Sicherheit betreffend, spielen ebenfalls eine Rolle, zumal sie zusätzlich geschürt werden durch ein reichhaltiges Kulturerbe an Märchen und Mythen mit meist negativen Assoziationen zum Wolf (Hunziker et al. 2001;Roskraft et al. 2007;Chapron et al. 2014;Dressel et al. 2015;Frank et al. 2015b;Linnell et al. 2017;Abb. 11.3). ...
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Zusammenfassung Die menschliche Perspektive in Mensch-Wildtier-Konflikten zu verstehen, ist wichtig für ein ganzheitliches Naturschutzmanagement, da solche Konflikte oft über die direkten Mensch-Tier-Begegnungen und deren negative Folgen hinausgehen. Stattdessen handelt es sich zumeist um Konflikte zwischen Menschen bzw. Interessengruppen („Stakeholder“), die verschiedene Ansichten zu Wildtieren und deren Management vertreten. Als „Human Dimensions of Wildlife“ (HDW) werden die menschlichen oder gesellschaftlichen Aspekte in Bezug auf Natur und Wildtiere bezeichnet. Als eine Teildisziplin der Naturschutzsozialwissenschaften („Conservation Social Sciences“) stellen sie einen inter- und transdisziplinären Ansatz dar, der vielfältige Sichtweisen zur Lösung komplexer Sachverhalte in das Naturschutzmanagement einbezieht. In diesem Kapitel stellen wir gängige HDW-Konzepte vor, illustrieren auf der Grundlage einschlägiger Literatur deren analytisches Potenzial und veranschaulichen praktische Anwendungen in Fallbeispielen zu Wölfen, Wildschweinen, Bibern und Fledermäusen. Abstract Understanding people in the context of human-wildlife conflict is important for integrated conservation management, as these conflicts often go beyond people and their direct encounters with or negative impacts from wildlife. They often include social conflicts among people or groups of people (i.e. stakeholders) that have different views about wildlife and how it should be managed. “Human Dimensions of Wildlife” (HDW) is a sub-discipline of “Conservation Social Sciences”, an inter- and transdisciplinary approach including multiple perspectives to solve complex conservation and management matters. Here, we provide an overview of frequently used HDW concepts and illustrate their predictive potential with evidence from the literature, while their practical application is demonstrated with examples and case studies on wolves, boars, beavers and bats.
... Previous analyses have identified a positive association between wildlife abundance trends and human development 10 and governance 9,23 . However, across the 1123 carnivore populations we studied, we found no effect of governance or human development on carnivore population trends (Fig. 3a), regardless of whether human development was modelled as a linear or quadratic term (Supplementary Table 6) 10 . ...
... Our study is focussed on population trends of large carnivores; a culturally important group 32 , essential for regulating ecosystem function 33 . Large carnivores represent an important study group as their population status is unclear, with reports of devastating declines 33 contrasted with remarkable recoveries 23 . Further, as a wellstudied taxa with abundant trend and trait datasets, large carnivores present a good system to evaluate important drivers of trends without being impacted by poor inference from missing data 34 . ...