Europes ecological backbone.pdf

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Integrated approaches to understanding mountain regions Wilderness attribute maps are combined using a simple weighted linear summation MCE model as follows: I = sum ∑ j( ) W w e ij j= n where: W sum = position on wilderness continuum w j = j th user-specified attribute weight e ij = standardised score n = number of attributes Other, more complex, MCE algorithms exist (Carver, 1991), but the weighted linear summation model has the advantages of simplicity and transparency. By applying different attribute maps and weights, different continuum maps can be produced reflecting different model and policy requirements. A reconnaissance-level wilderness map was produced for the Prague conference in May 2009. Map 10.4 is an updated version of this map using more up-to-date information supplied by the EEA, and has been developed using established methods of combining wilderness attributes as GIS data layers based on MCE techniques (Voogd, 1983; Carver, 1991; Fritz et al., 2000; Carver et al., 2002). The wilderness attributes used to inform the production of Map 10.4 were each mapped individually using the best available spatial datasets and are as follows: • Population density: data were derived from the Landscan global dataset (ORNL, 2010) see also Chapter 3. Population density is used here as an indicator of likely population pressure on the landscape; • Road density: derived from the Digital Chart of the World (DCW). This is the United States Defense Mapping Agency's (DMA) Operational Navigation Chart (ONC) 1:1 000 000 scale paper map series (DCW, 1992). While this dataset is not the most current, it has the advantage of being consistent across all European states. Road density was calculated using a 25 km radius kernel density filter and is used here as an indicator of not just road density, but also the likelihood of encountering other human structures such as bridges, dams, power lines, etc., as these are most often found alongside the road network; • Rail density: derived from the DCW, calculated using a 25 km radius kernel density filter and used here, as with road density, as an indicator of the density of the transportation infrastructure and associated human artefacts; • Distance from nearest road and railway line: individually derived from the DCW as separate attributes. Linear distance to the nearest road link and railway line are used as indicators of local remoteness and a proxy for likely visual influence on the landscape from modern human artefacts; • Naturalness of land cover: derived by reclassifying Corine land cover 2000 data (see Chapter 7) into a series of five naturalness classes. The 2000 dataset was used because, unlike the 2006 dataset, it includes data for all countries in Europe. The naturalness of land cover is used as an indicator of the likely level of human disturbance of natural ecosystem function and vegetation patterns; • Terrain ruggedness: derived from NASA's Shuttle Radar Telemetry Mission (SRTM) digital elevation model data at a resolution of 250 m. The Topographic Ruggedness Index (TRI) (Riley et al., 1999; Evans, 2004) was used to describe the difference in elevation between adjacent cells of a digital elevation grid. Terrain ruggedness is used here as a likely indicator of difficulty of the terrain and associated inaccessibility as well as an indicator of scenic grandeur. 10.3.3 Wilderness in Europe's mountain areas Numerous permutations of the above wilderness attributes are possible and can be combined using MCE using any number of weighting schemes to reflect particular desired outcomes or policies. The map shown in Map 10.4 is based on a simple equal‐weighted combination of population density, road density, distance from nearest road, naturalness of land cover and terrain ruggedness. The top 10 % wildest areas are defined on a simple equal area percentile basis and highlighted in blue. Comparing the resulting map against the distribution of mountain massifs (Map 10.5) demonstrates a high degree of correlation in the general pattern of the core wild areas. This is perhaps unsurprising given the inclusion of ruggedness, which is normally associated with mountainous landscapes. The alternative wilderness continuum map (Map 10.6) leaves out ruggedness and therefore may be more discriminating in its identification of wilderness mountain landscapes, but the underlying pattern of core high latitude and high-altitude areas remains, together with the more fragmented pattern of wildland areas dispersed across the remainder of Europe. The differences between Maps 10.5 and 10.6 appear mainly in the local detail in that the wilderness 196 Europe's ecological backbone: recognising the true value of our mountains
Integrated approaches to understanding mountain regions Map 10.4 Wilderness Quality Index (including terrain ruggedness) for Europe -30° -20° -10° 0° 10° 20° 30° 40° 50° 60° Wilderness Quality Index (including terrain ruggedness) for Europe 60° High 60° Low 50° 50° 40° 40° 0 500 0° 1000 1500 km 10° 20° 30° continuum map in Map 10.6 (which excludes terrain ruggedness criteria) becomes more fragmented. This has the result of similarly fragmenting the top 10 % wildest areas in this map, making them appear to cover a larger area when viewed at this broad scale. In addition, the removal of the ruggedness variable leads to a number of lowland areas in Finland being included among the top 10 % wildest areas. As the Wilderness Quality Index is a continuum, the most appropriate way to compare the extent of wilderness in different massifs and countries is with respect to the top 10 % wildest areas (referred to below as wilderness). Figure 10.4 shows wilderness areas relative to total area of each massif, and Figure 10.5 shows the wilderness areas as a percentage of the area of each massif. Clearly, the Nordic mountains contain by far the largest proportion (28 %) and area of wilderness of all mountain areas in Europe. While the total areas of wilderness are smaller in other massifs, there are notable proportions in other massifs including the Pyrenees (12 %), eastern Mediterranean islands and Alps (9 %), and British Isles (8 %). These patterns are comparable at the national scale (Figures 10.6 and 10.7). It is only in the Nordic countries that wilderness covers both very large areas of mountain land and quite high proportions of national mountain area (Norway 62 946 km 2 , 25 % of national mountain area; Sweden 30 180 km 2 , 33 %; Iceland 23 070 km 2 , 34 %); the only other country with more than 10 000 km² of wilderness is Spain (15 639 km 2 , 6 %). Nevertheless, what is also clear from Figures 10.6 and 10.7 is that, with the sole exception of Finland, wilderness is predominantly in mountain areas, even if the proportion of national mountain area that it covers is less than 10 % — except for the three previously mentioned Nordic countries as well as Hungary (18 %), Albania and Bosnia and Herzegovina (both 12 %), Slovenia (11 %), Ireland and Croatia (both 10 %). Europe's ecological backbone: recognising the true value of our mountains 197
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EEA Report No 6/2010 Europe's ecolo
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Cover design: EEA Cover photo © Ca
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Contents 7 Land cover and uses.....
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Acknowledgements Box 1.5: Maja Vasi
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Acknowledgements Chapter 8 Mountain
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Executive summary general, populati
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Executive summary Among all Europe'
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Introduction and background Interna
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Introduction and background same co
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Introduction and background instrum
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Introduction and background coopera
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Introduction and background instrum
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Introduction and background Box 1.4
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Introduction and background made. W
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Introduction and background Map 1.2
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Introduction and background require
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Introduction and background Map 1.3
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Mountain people: status and trends
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Mountain economies and accessibilit
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Ecosystem services from Europe's mo
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Climate change and Europe's mountai
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The water towers of Europe lowland
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The water towers of Europe Box 6.1
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The water towers of Europe Figure 6
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The water towers of Europe • Navi
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The water towers of Europe in the a
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The water towers of Europe (Livings
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Land cover and uses 7 Land cover an
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Land cover and uses Map 7.2 Dominan
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Land cover and uses Table 7.1 Distr
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Land cover and uses Figure 7.3 Land
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Land cover and uses Table 7.2 Chang
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Land cover and uses Box 7.1 Land us
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Land cover and uses Box 7.1 Land us
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Land cover and uses Figure 7.7 Aver
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Land cover and uses Box 7.3 Land re
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Land cover and uses Box 7.4 Land-us
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Land cover and uses Figure 7.11 Con
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Land cover and uses country before
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Land cover and uses Table 7.8 Natio
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Land cover and uses Map 7.6 Area cl
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Land cover and uses Box 7.6 Identif
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Land cover and uses Table 7.10 Tota
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Biodiversity 8 Biodiversity At the
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Biodiversity The geographic scope o
Page 148 and 149: Biodiversity Recognising that not a
Page 150 and 151: Biodiversity Figure 8.1 Number of m
Page 152 and 153: Biodiversity Figure 8.3 Habitats in
Page 154 and 155: Biodiversity For each habitat type,
Page 156 and 157: Biodiversity Box 8.2 Alien plants i
Page 158 and 159: Biodiversity Box 8.3 Climate change
Page 160 and 161: Biodiversity The pattern of success
Page 162 and 163: Biodiversity Box 8.4 Recent changes
Page 164 and 165: Protected areas Box 9.1 Sharr Mount
Page 166 and 167: Protected areas 9.1 Natura 2000 sit
Page 168 and 169: Protected areas Table 9.1 Area of N
Page 170 and 171: Protected areas the mountains of th
Page 172 and 173: Protected areas Figure 9.3 Distribu
Page 174 and 175: Protected areas Figure 9.7 Distribu
Page 176 and 177: Protected areas Figure 9.10 Changes
Page 178 and 179: Protected areas Box 9.3 Land use de
Page 180 and 181: Protected areas Figure 9.12 Percent
Page 182 and 183: Protected areas Box 9.4 Kaçkar Mou
Page 184 and 185: Protected areas Table 9.7 Area of n
Page 186 and 187: Protected areas scale. Overlaps are
Page 188 and 189: Protected areas Box 9.5 Cantabrian
Page 190 and 191: Integrated approaches to understand
Page 204 and 205: References References Chapter 1 All
Page 206 and 207: References European Union, 2010. Pr
Page 208 and 209: References European Commission, 200
Page 210 and 211: References Yoshino, M., 2005. Mount
Page 212 and 213: References World Resources Institut
Page 214 and 215: References Faggian, P. and Giorgi,
Page 216 and 217: References Lopez-Moreno, J.I.; Goye
Page 218 and 219: References Ambrosetti, W. and Barba
Page 220 and 221: References characteristics of the l
Page 222 and 223: References Rogora, M., 2007. 'Synch
Page 224 and 225: References Chapter 7 Aitchison, J.
Page 226 and 227: References Williamson, T., 2002. Th
Page 228 and 229: References Lelek, A., 1987. The fre
Page 230 and 231: References Berthold, P., 2000: Voge
Page 232 and 233: References years of simulated clima
Page 234 and 235: References Konstanz, erschienen 199
Page 236 and 237: References EEA/UNEP, 2004. High nat
Page 238 and 239: References Innovations in GIS 7. Ta
Page 240 and 241: Appendix 1 Species Annex Endemic to
Page 246 and 247: Appendix 2 Appendix 2 Mountain habi
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Appendix 2 Code ( a ) Name Category
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Appendix 2 Code ( a ) Name Category
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TH-AL-10-006-EN-C doi:10.2800/43450
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