130925-studie-wildlife-comeback-in-europe

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Constructing historical distribution maps – pitfalls, biases and advances in technology As with population time series, knowledge of both historical and current species distributions can help underpin understanding of wildlife comeback and declines and help to provide tangible solutions to conservation issues [1] . While locality records for species are generally widely available, for example through museum data, literature data, atlas publications and online databases [2] , reconstructing species distributions over time often relies on a variety of sources, each of which may harbour distinct biases and shortcomings, which in turn may have a direct bearing on the accuracy and resolution of the resulting distribution map. Compared to other regions of the world, European wildlife has received a large amount of research attention over time. As a result, there is a large pool of knowledge available on current species occurrences and distributions, and obtaining current data is made even more straightforward through the establishment of records centres and databases which contain up-to-date information. In the case of the IUCN Red List of Threatened Species [3] , current distribution maps are verified by experts and regularly updated; moreover, the data are freely available. Construction of historical distributions is much less straightforward. Most often, distributions are amalgamated from different sources, and this can lead to biases within the resulting distribution data. It is therefore imperative to understand the shortcomings when constructing historical distributions, many of which have been discussed in the literature (see [2] for a good overview). Here, we summarise the three most likely pitfalls when reconstructing historical distribution maps: 1. Data from different sources are likely to vary in terms of spatial resolution and may be biased towards certain parts of the species range, while other areas within the species range may only be broadly covered or even overlooked. 2. The age of technology has advanced our ability to map species distributions: while in the past, distribution estimates were generally based on species occurrence records and broad inferences about suitable habitat, we now have the use of advanced habitat suitability models which are fed by detailed data layers on climatic and habitat factors. For example, some mammalian range maps on the IUCN Red List have been produced that way. This creates a dichotomy in spatial resolution between current and past range maps. 3. Focus on previously understudied taxa (for example as a result of increased conservation focus) may have led to recent discoveries of new populations and locations. Such new records suggest range expansion, while in fact the species may have persisted in that location undiscovered for a long period of time. Certain precautions can be taken to avoid these pitfalls and biases in the resulting data. For example, smoothing of overly detailed distribution maps may help to find some middle ground between different spatial resolutions. Including areas for which species presence is uncertain in our construction of historical distributions can help to reduce bias towards overstudied areas. However, these sources of bias remain a major issue when considering range changes from historical baselines. References 1. Willis, K.J., Arauo, M.B., Bennett, K.D., et al. 2007. How can a knowledge of the past help to conserve the future? Biodiversity conservation and the relevance of long-term ecological studies. Philosophical Transactions of the Royal Society of London B, 362: 175–186. 2. Boakes, E.H., McGowan, P.K.J., Fuller, R.A., et al. 2010. Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biology, 8: e1000385. 3. IUCN. 2011a. The IUCN Red List of Threatened Species. 2011.1 Edition. [Available from: http:// www.iucnredlist.org/. the Waterbird Population Estimates (WPE) information portal [18] , an online database providing information on the current status of waterbird species, including long-term population trend analyses carried out using TRIM software [19] . Pan-European trends of breeding population size for two species (White stork and Common crane) are available in the form of Pan-European Common Bird Monitoring Scheme (PECBMS) Index trends [20] . PECBMS is a joint initiative of the European Bird Census Council (EBCC) and BirdLife International, which aims to collate data on the breeding population trends of common well-monitored species in Europe. PECBMS combines the results of national bird monitoring schemes to produce yearly population indices of bird species across Europe, using TRIM software [19, 21] . The method takes into account differences in survey methodologies between countries, as well as differences in population size, and imputes any missing values for survey localities and years [21] . It was possible to adapt this method to calculate pan-European trends in abundance for five raptor species [White-tailed eagle (Haliaeetus albicilla), Eastern Imperial eagle (Aquila heliaca), Lesser kestrel (Falco naumanni), Red kite (Milvus milvus) and Peregrine falcon (Falco peregrinus)], using the time-series of estimated population size and treating each 16
IUCN Red List Criteria A Reduction in population size B Small range – fragmented, declining or fluctuating C Small population – declining or fluctuating D/D1 Very small population D2 Very small range E Quantitive analysis – probability of extinction IUCN Red List Category Global Regional/national Extinct Extinct EX Extinct in the Wild Extinct in the Wild EW Regionally Extinct RE Critically Endangered Critically Endangered CR Endangered Endangered EN Vulnerable Vulnerable VU Near Threatened Near Threatened NT Least Concern Least Concern LC Data Deficient Data Deficient DD Not Applicable NA Not Evaluated Not Evaluated NE driven declines and that continue to affect the European populations, as well as the conservation actions that enabled or contributed to recovery, were identified from the literature. SAPs were a key source for this information for birds, as they aim to identify priorities for conservation action and document limiting factors and threats. For mammals, ancillary data on threats and conservation actions were extracted from the information provided for populations underlying the abundance trend, species-specific literature and communication with species experts. Threats and conservation actions were classified according to the IUCN Threat and [30, 31] Conservation Actions Classification schemes to ensure comparability across species (note that only conservation actions linked to positive change are included in the tables accompanying the mammal species accounts, although threats responsible for declines are discussed in the text). These classification schemes follow a hierarchical structure of comprehensive and exclusive upper level categories and expandable lower level categories, which can be easily scaled, and aim to standardise descriptions of direct threats and conservation actions for systematic use in conservation projects [32] . Threats are classified into twelve upper level categories, including residential and commercial development, agriculture and aquaculture, transportation and service corridors (e.g. roads and railroads, utility and service lines), biological resource use (direct and indirect effects of hunting, fishing and harvesting), natural system modifications, pollution, and climate change and severe weather [30] . Conservation actions are classified into the following upper level categories: Land/water protection, Land/water management, Species management (e.g. reintroduction, ex-situ conservation), Education and awareness, Law and policy, and Livelihood, economic and other incencountry as a survey locality. Trend output was smoothed using the tool TrendSpotter, which uses a structural time-series model in combination with the Kalman filter to smooth trends [22–24] . For the remaining species, data were either too sparse to produce meaningful trends, or constituted complete population censuses, for which an overall trend would not contribute any additional value. Ancillary data on threats and conservation actions The IUCN Red List of Threatened Species [4] is a key tool for biodiversity conservation, providing a framework for the classification of animal and plant species according to their risk of extinction in order to inform conservation efforts. Each species’ extinction risk is classified based on a range of quantitative criteria (Figure 2). Threatened species are listed as Vulnerable (VU), Endangered (EN) or Critically Endangered (CR) according to quantitative thresholds. More details on the IUCN Categories and Criteria and their application can be found on the IUCN website [25] . The IUCN Red List Categories and Criteria assess the global extinction risk of species, but the framework can also be used for regional and national assessments [26] . The status of bird species has been evaluated by BirdLife International at a global [27] , pan-European [9] and European Union (EU) scale [28] . Similarly, mammals have been assessed at the global level [29] and at the pan-European/EU scale [1] . At the European scale, the conservation status of species is evaluated against various quantitative criteria (including the IUCN system; Figure 2). Birds are classified at the European scale as Favourable (Secure) or Unfavourable (classified as Threatened Globally, Declining, Rare, Depleted, or Localised) (Table 3). Following this assessment, species are classified into categories of Species of European Conservation Concern (SPECs) and Non-SPECs (Table 4). For each species, the main threats that have Figure 2. IUCN Red List Categories and Criteria for assessing species’ extinction risk at the global and regional/national level [25, 26] . 17
Page 1 and 2: Stefanie Deinet Christina Ieronymid
Page 3 and 4: Wildlife comeback in Europe The rec
Page 5 and 6: Table of contents Foreword . . . .
Page 7: Foreword Shifting baselines In Euro
Page 10 and 11: The Adriatic coastline of the Veleb
Page 12 and 13: 96 year old olive farmer with his d
Page 14 and 15: 12
Page 16 and 17: Limitations of population trend dat
Page 20 and 21: Red deer at the Oostvaardersplassen
Page 22 and 23: Table 2. Definitions of classificat
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Page 26 and 27: 3.1. European bison Bison bonasus S
Page 28 and 29: Table 2. Latest population estimate
Page 30 and 31: Figure 1c. Map highlighting areas o
Page 32 and 33: Rank Reason for change Description
Page 34 and 35: 3.2. Alpine ibex Capra ibex Summary
Page 36 and 37: Figure 1a. Distribution of Alpine i
Page 38 and 39: % change 1000 800 600 400 200 0 Fig
Page 40 and 41: 3.3. Iberian ibex Capra pyrenaica S
Page 42 and 43: Figure 1a. Distribution of Iberian
Page 46 and 47: 3.4. Southern chamois Rupicapra pyr
Page 48 and 49: Figure 1a. Distribution of Southern
Page 52 and 53: 3.5. Northern chamois Rupicapra rup
Page 54 and 55: Scale Status Population trend Justi
Page 56 and 57: % change 80 60 40 20 0 and Italy [2
Page 58 and 59: Subspecies balcanica Exploitation B
Page 60 and 61: 3.6. Eurasian elk Alces alces Summa
Page 62 and 63: Figure 1a. Distribution of Eurasian
Page 64 and 65: Poland [10] and Estonia [28] . It i
Page 66 and 67: References 1. Geist, V. 1998. Deer
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3.7. Roe deer Capreolus capreolus S
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Estimate Year assessed Reference Gl
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Figure 2. Change in Roe deer popula
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Recent developments As discussed ab
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3.8. Red deer Cervus elaphus Summar
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% change 750 600 450 300 150 0 Figu
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lineages for the local area and min
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3.9. Wild boar Sus scrofa Summary T
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Abundance and distribution: changes
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References 1. IUCN 2011a. The IUCN
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3.10. Golden jackal Canis aureus Su
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Recent developments Table 3. Major
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3.11. Grey wolf Canis lupus Summary
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Drivers of recovery Figure 2. Distr
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References 1. Mech, L.D. & Boitani,
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3.12. Eurasian lynx Lynx lynx Summa
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% change 750 600 450 300 150 0 Figu
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Figure 3. Map of recent development
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3.13. Iberian lynx Lynx pardinus Su
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Figure 1a. Distribution of the Iber
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Table 3. Major reasons for positive
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18. IUCN 2011b. European Red List.
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3.14. Wolverine Gulo gulo Summary T
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Figure 1a. Distribution of Wolverin
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Rank Reason for change Description
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3.15. Grey seal Halichoerus grypus
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Estimate assessed Reference Global
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% change 1500 1200 900 600 300 0 Fi
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3.16. Harbour seal Phoca vitulina S
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east coast, the distribution is res
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% change 200 150 100 50 0 populatio
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Figure 3. Map of recent development
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3.17. Brown bear Ursus arctos Summa
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Table 2. Latest population estimate
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144
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Recent developments % change 200 15
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mation, e.g. between Slovenia and C
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3.18. Eurasian beaver Castor fiber
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% change 20,000 16,000 12,000 200 1
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Table 3. Major reasons for change i
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158
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4.1. Pink-footed goose Anser brachy
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Table 2. Major threats that drove P
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4.2. Barnacle goose Branta leucopsi
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Figure 2. Current breeding and wint
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Table 3. Conservation actions in pl
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4.3. Whooper swan Cygnus cygnus Sum
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Threat Description Impact Hunting a
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4.4. White-headed duck Oxyura leuco
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No. of individuals 5000 4500 4000 3
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educing the population in the count
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4.5. White stork Ciconia ciconia Su
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Country No. of breeding pairs Trend
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Action Description Impact Monitorin
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4.6. Eurasian spoonbill Platalea le
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Country No. of breeding pairs No. o
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Threat Description Impact Residenti
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4.7. Dalmatian pelican Pelecanus cr
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194
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Table 3 Major threats that drove th
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4.8. Lesser kestrel Falco naumanni
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Figure 2. Current distribution of L
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Action Description Impact Livelihoo
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4.9. Saker falcon Falco cherrug Sum
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Table 2. Latest Saker falcon popula
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Action Description Impact Planning
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4.10. Peregrine falcon Falco peregr
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No. of breeding pairs 1,600 1,200 8
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Action Description Impact Legislati
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4.11. Red kite Milvus milvus Summar
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Country No. of breeding pairs Trend
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4.12. White-tailed eagle Haliaeetus
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Country No. of breeding pairs Year
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References 1. Bijleveld, M. 1974 Bi
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4.13. Bearded vulture Gypaetus barb
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Figure 2. Current distribution of B
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4.14. Griffon vulture Gyps fulvus S
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Figure 2. Current distribution of G
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4.15. Cinereous vulture Aegypius mo
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Figure 2. Current distribution of C
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4.16. Spanish imperial eagle Aquila
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Threat Description Impact Transport
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Drivers of recovery The spectacular
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4.17. Eastern imperial eagle Aquila
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Figure 2. Current distribution of E
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252
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4.18. Common crane Grus grus Summar
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Figure 2. Current breeding and wint
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Action Monitoring and planning Site
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4.19. Roseate tern Sterna dougallii
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Threat Description Impact Human int
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264
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337,539 2,000 20,000 >163,750 % abu
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1950s 1980s Present 50km grid Speci
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No. of species 1 2 3 4 5 6 > 6 No.
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272
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A Range change B Range change C Ran
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Reason for positive change Species
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Dalmatian pelicans at the Kerkini L
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280
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The comeback of large and charismat
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A safari group in the Velebit mount
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The view from a bear watching hide
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species [44] [45] and if animals be
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One of the challenges around increa
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Box 1. Return and urbanization of w
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Table 1. Livestock damage by mammal
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key tool for wildlife population in
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Some of the over 500,000 visitors a
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Box 2. The native versus alien spec
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References 1. Navarro, L.M. and H.M
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113. Potena, G., et al., Brown Bear
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Appendix 1. Sources of distribution
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Acknowledgements This study on wild
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