International Journal of Biodiversity Science, Ecosystem Services & Management

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The use of detailed biotope data for linking biodiversity with ecosystem services in Finland Petteri Vihervaara , Timo Kumpula , Anni Ruokolainen , Ari Tanskanen & Benjamin Burkhard To cite this article: Petteri Vihervaara , Timo Kumpula , Anni Ruokolainen , Ari Tanskanen & Benjamin Burkhard (2012) The use of detailed biotope data for linking biodiversity with ecosystem services in Finland, International Journal of Biodiversity Science, Ecosystem Services & Management, 8:1-2, 169-185, DOI: 10.1080/21513732.2012.686120 To link to this article: http://dx.doi.org/10.1080/21513732.2012.686120

Published online: 15 May 2012.

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Date: 28 January 2017, At: 23:56

International Journal of Biodiversity Science, Ecosystem Services & Management Vol. 8, Nos. 1–2, June 2012, 169–185

The use of detailed biotope data for linking biodiversity with ecosystem services in Finland Petteri Vihervaaraa*, Timo Kumpulab , Anni Ruokolainenb , Ari Tanskanenb and Benjamin Burkhardc a

Finnish Environment Institute (SYKE), P.O. Box 111, Joensuu 80101, Finland; b Department of Geography and Historical Studies, University of Eastern Finland, Joensuu 80101, Finland; c Institute for the Conservation of Natural Resources, Christian Albrechts University Kiel, Kiel 24098, Germany It has been widely accepted that ecosystem services (ESs) should be taken into account in natural resource management decisions. Hence, there is an increasing need for innovative quantification methods and tools to evaluate ESs on different landscape scales, and under varying land-use forms. Integrating biodiversity protection with the provision of ESs is a key element for sustainable land-use planning. Geographic Information Systems (GIS) and spatial analysis, together with various environmental data, provide a suitable foundation for ESs evaluations. Recent advances in earth observation technologies have supported land-cover-based ESs mapping on global, regional and local scales. Global and regional land-cover maps can help in coarse assessments of some biophysical characteristics of the environment, but they cannot provide exact information about local biodiversity and biotope types that form the base of ESs supply. Therefore, more detailed tools such as aerial photographs and field surveys are needed. High-quality biotope data are usually fragmentary or absent for private land in Finland, but are available for most state-owned commercial forests and protection areas. We tested the use of biotope data derived from aerial photographs and an extensive field inventory to map ESs in natural protection areas in northern Finland. We argue that protection areas, where large and long-term databases have been collected, offer excellent study sites to develop further the methodology for integrating coarse-scale remote-sensing data, such as CORINE, with more detailed ecological and structural data collected from aerial photographs and ground surveys. In addition, the use of detailed biotope data supports the linkage of biodiversity information with landscapes’ capacities to provide ESs. Different data sources will improve the management of protection areas, thereby optimizing multiple land-use objectives. Keywords: biodiversity; biotope classification; conservation; ecosystem service mapping; environmental management; GIS; habitat

Introduction Ecosystems provide a wide range of goods and services (hereafter called ecosystem services (ESs)) to people (de Groot et al. 2010). It is widely accepted that they should be taken into account in natural resource management decisions, but one key problem is the difficulty of quantifying their supply levels appropriately, as well as assessing their respective values (Naidoo et al. 2008; Kienast et al. 2009; Nelson et al. 2009). Integrating biodiversity protection with the provision of ESs is a key element for sustainable land-use planning, as it is also stated in the new European Union Biodiversity Strategy to 2020 (EC 2011). Biodiversity is the basis for many ecosystem processes, such as species interactions, and cycling of energy and material in trophic networks, and, therefore, underpinning the provision of ESs depending on them, such as pollination and nutrient cycling, just to mention some examples (MA 2005; Anderson et al. 2009; Feld et al. 2009; Kumar 2010). But still, the role of biodiversity for ecosystem functioning is scarcely known (Loreau et al. 2002; Hooper et al. 2005), and understanding of the relationships of biodiversity, ESs and land use is in its initial phase (Haines-Young 2010; Vandewalle et al. 2010). The interest in ESs has been increasing steadily during the last decades, but appropriate applications at scales relevant for *Corresponding author. Email: [email protected] ISSN 2151-3732 print/ISSN 2151-3740 online © 2012 Taylor & Francis http://dx.doi.org/10.1080/21513732.2012.686120 http://www.tandfonline.com

decision-making, as in our case management of regional scale that is regulated at national scale, are still lacking (Egoh et al. 2007; Daily et al. 2009). There is a consensus that biodiversity and ESs are interrelated (e.g. European Academies Science Advisory Council 2009; Kumar 2010), but how the varying biodiversity and ES categories are linked with each other is not yet clear. It has been stated that (1) local species richness has a positive influence on some ESs; (2) species richness alone did not increase ESs, which resulted in the fact that biodiversity conservation was not fully a synonym for ES protection; (3) several factors affect the supply of ESs, and biodiversity is just one of those factors; (4) nature protection areas can provide many valuable ESs, but some other areas can provide some ESs more efficiently; (5) it is possible to increase ESs via habitat management and restoration (Srivastava and Vellend 2005; Balvanera et al. 2006; Cardinale et al. 2006; Rafaelli 2006; Hector and Bagchi 2007; Benayas et al. 2009). In ecosystems that are managed in order to emphasize the delivery of one specific ES (e.g. timber production), the supply of other services can be negatively affected (Kumar 2010; see Nelson et al. 2011). ES concretize how human well-being is dependent on ecosystem functions and biodiversity and vice versa,

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to assess how land use and other human-based pressures influence ecosystems and biotopes (MA 2005; Swetnam et al. 2011). Maps are a powerful tool to aggregate spatial data on land-use, biodiversity and ecosystem functioning into ES supply information (Burkhard et al. 2011). This information can be transferred into environmental data sets which can be processed with Geographic Information Systems (GIS) and statistical analysis methods, supplying information, such as distribution maps of biodiversity and ESs, needed for environmental conservation and landscape management (GrêtRegamey et al. 2008; Nelson et al. 2009; Vihervaara et al. 2010). Other spatial approaches for nature conservation optimization, based on site connectivity, habitat structure and species distributions, have been developed (e.g. Moilanen et al. 2005; Egoh et al. 2008; Lehtomäki et al. 2009). Recent advances in earth observation technologies have supported land-cover-based ES mapping on global, regional and local scales. Global and regional land-cover maps are based, for example, on the Advanced Very High Resolution Radiometer (AVHRR) (Loveland et al. 2000), SPOT-Vegetation (e.g. GLC2000 project, see Bartholomé and Belward 2005), and the Moderate Resolution Imaging Spectroradiometer (MODIS) (Friedl et al. 2002) data in 1 km grid resolution; Medium Resolution Imaging Spectrometer (MERIS) sensor data of ENVISAT satellite (e.g. GlobCover project, see Arino et al. 2007) in 300 m grid, and the European CORINE Land Cover database (based on Landsat satellite imagery) (Bossard et al. 2000; EEA 2002) in 25 m grid. They can help in coarse assessments of some biophysical characteristics of the environment, but they cannot really provide detailed information at the local scale (less than 1/2 ha patch size) about biodiversity and biotope types, which are important for the understanding of ecosystem functions and related service supply. More detailed tools, such as aerial photographs, very high-resolution satellite imagery (e.g. Worldview-2, Quickbird-2, grid size 0.5–2 m), and field surveys, are needed to take biodiversity and biotope data into account, and to link them with ESs. In the case of Finland, the availability of detailed biotope data is very patchy or mainly absent in private forests and peatlands. However, detailed biotope data are available in our study area nowadays from most state-owned commercial forests and protection areas managed by Metsähallitus (Finnish Forest and Park Service). The quantification and mapping of ESs have been mentioned as major challenges for the implementation of ESs in decision-making (Daily and Matson 2008). Several ES mapping approaches and models have recently been derived and applied at different scales, Naidoo et al. (2008), Boumans et al. (2002) and Costanza et al. (1997) have all worked on the global scale; Maes et al. (2011), Kienast et al. (2009) or Haines-Young et al. (2011) derived ES maps on the continental European scale; Klug and Jenewein (2011), Willemen et al. (2008), Troy and Wilson (2006) and Burkhard et al. (2009, 2011) worked on regional scales; Posthumus et al. (2010) and Nedkov and Burkhard

(2011) calculated selected ESs on floodplain and watershed scales. For short reviews of currently existing ES mapping approaches, see Bolliger and Kienast (2010) or Burkhard et al. (2009). In this article, we will use the cases of the Urho Kekkonen National Park and the Sompio Strict Nature Reserve in northern Finland as test areas for applied ES mapping. This approach is applicable to the whole of Finland, and could be adopted rather easily all over Europe, owing to the availability of similar kinds of databases (e.g. EUNIS). This article is a continuation of the rather coarse scale (mainly CORINE data based) ES assessment presented in Vihervaara et al. (2010), now using more detailed biotope data. Thereby, we will (1) show how suitable detailed biotope classification data are for ESs mapping, compared with other available data sets (such as CORINE); (2) demonstrate mapping of some selected ESs, and assess the reliability of the results and (3) evaluate how integrating biodiversity information with ESs data may improve the management of ESs. Materials and methods Study area Our study area is located in northern Finland and consists of a part of the Urho Kekkonen (UKK) National Park (area 2550 km2 ) and the Sompio Strict Nature Reserve (179 km2 ), which share a mutual border (Figure 1). The area has a wide variety of different natural landscapes from mires in the south, river valleys to the north and mountainous regions in the central parts. Reindeer herding and tourism are the key sources of livelihood for those using the area nowadays. Walking trails allow hiking in the western parts of the UKK park, and this is one of the most popular hiking areas in Finland with over 180,000 visitors per year. Fishing and hunting are allowed with a permit, but only to local community residents, and berry-picking is free for everyone. The Sompio reserve was established for nature protection, but walking on trails is allowed. In Sompio, camping is only allowed in certain places. Forestry, which has been a major land-use form in northern Finland with significant impact on the landscape, is prohibited in both areas. The same applies for mining, military or agricultural activities. The UKK park and Sompio belong both to the European Natura 2000 network. Fourteen biotope types that are listed in the EU Habitats Directive (92/43/EEC) can be found here: western taiga (Natura 2000 habitat code: 9010; surface area 30%), aapa mires (7310; 20%), Nordic subalpine/subarctic forests with Betula pubescens ssp. czerepanovii (9040; 11%), siliceous rocky slopes with chasmophytic vegetation (8220; 5%), alpine and boreal heaths (4060; 5%), bog woodland (91D0, 5%) and small percentages of other habitat types. Endangered species, listed in the Habitats Directive Annex II, found in the area are wolverine (Gulo gulo), otter (Lutra lutra), river

International Journal of Biodiversity Science, Ecosystem Services & Management

Figure 1.

Location of the study area.

pearl mussel (Margaritifera margaritifera), moth (Xestia borealis), yellow marsh saxifrage (Saxifraga hirculus), grove sandwort (Moehringia lateriflora), wideleaf polargrass (Arctagrostis latifolia), Lapland buttercup (Ranunculus lapponicus), moss (Cynodontium suecicum) and hematocaulis moss (Hamatocaulis vernicosus). Twenty-nine bird species mentioned in the Birds Directive’s (2009/147/EC) Annex I are found in the area, and five of them are endangered, they are peregrine falcon (Falco peregrinus), gyrfalcon (Falco rusticola), merlin (Falco columbarius), golden eagle (Aquila chrysaetos) and the white-tailed eagle (Haliaeetus albicilla) (Metsähallitus 2001).

Types of conservation areas in Finland Finnish nature conservation is governed by international and national environmental policies, such as EU legislation (e.g. Habitats and Birds Directives), laws of nature protection and forest protection agreements. In Finland, there is a representative network of protection areas covering the main biotopes, including national parks, strict nature reserves (i.e. nature parks), Natura 2000 sites and also voluntary conservation areas (called YSA, i.e. private conservation areas). In northern Finland,

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there are also so-called wilderness areas. About 9% of the total area of Finland is protected under the Nature Conservation Act or the Act for the Protection of Wilderness Reserves. The Finnish government has approved seven nature conservation programmes, covering 1,809,500 ha (together with other protected areas) of the following types: (1) national parks and strict nature reserves, (2) mires, (3) bird wetlands, (4) eskers, (5) herb-rich woodlands, (6) shores and (7) old-growth forests. Twelve wilderness reserves have been established in northern Lapland, covering 1,489,000 ha. Most of Finland’s protected areas also belong to the EU’s Natura 2000 network of protected areas, which cover 17% of the EU’s surface (EEA 2010; Finnish Environment Institute 2011). ES alone have seldom been the reason for the establishment of nature conservation areas, but conservation decisions have been based on biodiversity, biotope and landscape scenic values. Habitat quality, spatial coverage and connectivity are the three key elements of a good network of conservation areas (e.g. Moilanen et al. 2005). Nature protection plays a crucial role in conservation of biodiversity and endangered species. The effectiveness of the conservation areas depends on the protection act, local restrictions and the management plan designed for each site. In Finland, conservation areas can be classified into: (1) large-scale, semi-strictly protected areas, such as wilderness areas and national parks; (2) large-scale, strictly protected areas, such as nature parks (i.e. strict nature reserves), including some Natura 2000 sites; (3) smallscale, strictly protected areas, such as special conservation programmes, mostly Natura 2000 sites and (4) nonprotected areas in which biodiversity and ESs can be protected using other policy tools; for example, key habitats of the Forest Act, or those protected voluntarily with guidelines for sustainable forestry, or, for instance, payments for ESs (PES) schemes (Finnish Environment Institute 2011). Biotope classification Biotope (used here synonymously with habitat) means an area of land or water with uniform environmental conditions and biota, described by community structure and species assemblages (e.g. Raunio et al. 2008). In Finland, three types of biotope classifications have been used recently, which cover the variety of prevailing biotopes. These are presented in the following publications: (1) General classification system for Finland’s biotopes (Tuominen et al. 2001) (Table 1). (2) Natura 2000, habitats manual (Airaksinen and Karttunen 2001). (3) Assessment of threatened habitat types in Finland (Raunio et al. 2008). All these classifications have adapted the basic structure and defining criteria used in the European Nature

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Table 1. The tested biotope classification divides Finland’s biotopes into 10 main categories. A B Ca Da Ea Fa Ga Ha Ia Ja

Marine biotopes Coastal biotopes Freshwater biotopes Densely wooded forest biotopes Sparsely wooded heathland and rocky biotopes Open and sparsely wooded wetland biotopes Open heathland, meadowland, and scrubland biotopes Open rocky biotopes Agricultural biotopes Biotopes in built-up areas

Notes: Hierarchy of classes is designed for a maximum of four levels. Classification units at levels 1–3 can normally be distinguished from aerial photographs and maps alone, whereas at level 4 very detailed aerial photographs or fieldwork are often required (Tuominen et al. 2001). a The category is found from the study area.

Information System’s (EUNIS) classification (Davies and Moss 1997; Davies et al. 2004). The EUNIS habitat types’ classification is a comprehensive pan-European system to facilitate the harmonized description and collection of data across Europe, through the use of criteria for habitat identification. It covers all types of habitats, from natural to artificial, terrestrial to freshwater and marine. EUNIS data are collected and maintained by the European Topic Centre on Biological Diversity for the European Environment Agency, and the European Environmental Information Observation Network, to be used for environmental reporting and for assistance in the Natura 2000 process (EU Habitats and Birds Directives). Biotope classifications are based on maps and aerial photographs combined with ground surveys. Traditionally, very detailed vegetation classifications have also been used in Finland, especially for forests and mires (Cajander 1926; Laine and Vasander 1990). In this article, we have used the general classification system for Finland’s biotopes (Tuominen et al. 2001; based on its earlier test version; Toivonen and Leivo 1993), because that forms the basis of the data set we used in our study area. This general classification was developed and tested for the first time for the UKK National Park (Sihvo 2002). Nowadays, it is available for many state-owned protection areas in Finland. Natura 2000 biotope data (i.e. Airaksinen and Karttunen 2001) are also available for all protected areas that belong to the Natura 2000 network. Still, we decided to use the general one, because of its potential to be used outside the protection areas in the future. The threatened habitats classification (Raunio et al. 2008) would include the most detailed biotope types, but its coverage is, so far, very patchy and the inventory system is still under development. However, this classification is promising for future studies, because of its biological accuracy. For our analysis, the original 111 detailed general biotope classes (on the fourth most detailed hierarchical level of the classification system, belonging to 31 classes

on the second level) found in the study area were reclassified into 17 classes to keep the number of compared classes in a reasonable size while it still includes important biodiversity details (Tables 1 and 2). The reclassification of forest types was done by determining the dominant tree species (pine, deciduous or mixed) and the age of the forest (young, old and regenerating). Both sparse forests (forest canopy coverage 10–30%) and dense forests (canopy coverage >30%) were merged into the same classes. Mountain-birch forests were placed in their own class. Comparatively, the number of CORINE classes located in the study area was 10. Based on these classes, variations were studied when using different background data.

GIS data sets and methods Metsähallitus manages the state-owned land property and collects and provides environmental data sets based on forest and nature inventories, and ecosystem assessments on these lands. The results and data are stored in the SutiGis database which covers about 8 million hectares of state-owned land. The general biotope data in SutiGis were created by digitizing homogenous patterns from hundreds of aerial photographs (1 m resolution) into vector format (about 1.5 million forest polygons). Each biotope pattern type was ground validated and, if necessary, corrected. The scale of SutiGis in northern Finland is 1:20,000. The SutiGis database was introduced in 2001 and it is used and updated continuously by the nature protection and management authorities, and in local and regional planning activities. The GIS analysis and the map production were done using ArcGIS 10 Desktop software. In addition to the general biotope data evaluation, we compared our results with the European-wide CORINE land-cover database (CLC 2000) that has been applied and tested for ESs mapping in recent research projects (Burkhard et al. 2009; Vihervaara et al. 2010; Maes et al. 2011). CORINE is a land-cover/land-use data set product produced by the European Environment Agency, and is available for most of the European countries (EEA 2002). The Finnish CORINE land-cover data used in this study has a grid size of 25 m, but the actual resolution is coarser as it is originally based on Landsat TM imagery with 30 m resolution.

Evaluated ES and expert judgements We evaluated the supply capacity for 18 ESs classes (Table 3 and Figure 2) within the detailed biotopes and the CORINE land-cover types. The evaluated ESs was divided into three main groups – provisioning, regulating and cultural services – mainly based on the methodology of the MA (2005). The evaluation of the different ESs classes was based on the judgements of six experts; all researchers were familiar with the environmental and social conditions of the study area. The ESs supply capacities were assessed on a six-step scale ranging from 0 to 5, where 0 presented

Gyrfalcon, merlin, Cynodontium suecicum Otter, yellow marsh saxifrage, grove sandwort Yellow marsh saxifrage, wideleaf polar grass Peregrine falcon, white-tailed eagle, yellow marsh saxifrage, hematocaulis moss Wolverine, gyrfalcon Wolverine

0.1 7.9 0.0

33

10.3

20,008 121 15,252

1.9

0.0

0.1 21.1 10.0

3615

64

190 40,791 19,353

0.9 14.0

1652 27,134

Wolverine, golden eagle, Xestia borealis

1.4

2795

Lapland buttercup

3.7 26.8 0.5

1.0 0.2

7167 51,897 969

2023 361

Percentage of the study area

Wolverine, golden eagle

Otter, river pearl mussel River pearl mussel, yellow marsh saxifrage, wideleaf polar grass

Area (ha)

–

8110 3230, 4060, 6150

71xx, 72xx, 73xx

91D0, 71xx

7240, 7140

– 3230, 4060, 4080, 6150, 9040 8110, 82xx

– 9010, 9050

9010, 9050

– 9010 –

31xx 3220, 3260, 3210

Comparable Natura 2000 biotopes (Airaksinen and Karttunen 2001)

Notes: Explanations for original classification codes can be found in Tuominen et al. (2001) and Airaksinen and Karttunen (2001). Relationships of biotopes with some endangered species are also presented.

15. Barren mountain tops 16. Open mountain biotopes (fjäll heaths) 17. Infrastructure

13. Mires, peatlands with forest/bushes 14. Open mires and peatlands

12. Riparian/shore biotopes

9. Regenerating forests 10. Mountain birch – pine, shrubs 11. Rocks, boulders, cliffs (steeps)

3. Young pine dominated forest 4. Old pine dominated forest 5. Young broad-leaved deciduous dominated forest 6. Old broad-leaved deciduous dominated forest 7. Young mixed forest 8. Old mixed forest

1. Lakes and rivers 2. Springs and streams

Endangered species (EU Habitats Directive; Annex II or Birds Directive Annex I) associated with biotope class, some examples

Biotope classification used in the ES analysis and its comparison with comprehensive data sets.

Biotope classes

Table 2.

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Table 3. Ecosystem service provision potential values per biotope: ‘Average provisioning’ (nine provisioning services); ‘Average regulating’ (four regulating services) and ‘Average cultural’ (three cultural services). 1 Reindeer Game Fish Berries fungi Medicines Wood Water Energy Genetic resources Average provisioning Local and regional climate Carbon storage Pollination Erosion prevention Average regulating Local and Sami cultures Esthetic landscape Recreation Average cultural

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

1 4 5 0

1 1 5 0

2 2 0 2

5 4 0 4

4 3 0 2

4 4 0 4

3 3 0 2

4 4 0 5

2 2 0 1

4 4 0 3

1 3 0 1

4 4 2 3

4 4 0 5

3 3 0 4

1 2 0 0

5 4 0 3

0 0 0 0

0 0 5 4 3

0 0 5 2 3

0 3 0 2 2

0 5 0 4 3

0 3 0 2 2

0 5 0 4 3

0 3 0 2 2

1 5 0 4 3

0 3 0 2 1

0 2 0 1 3

0 0 0 0 3

2 2 0 1 3

2 1 1 4 3

2 0 1 3 3

0 0 0 0 1

0 0 0 0 3

0 0 0 0 1

2.4

1.9

1.4

2.8

1.8

2.7

1.7

2.9

1.2

1.9

0.9

2.3

2.7

2.1

0.4

1.7

0.1

2

1

2

2

2

2

2

2

1

1

2

1

2

2

1

1

1

1

1

2

3

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2

3

1

2

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2

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0

0 0

0 0

2 2

2 4

3 2

3 4

3 2

3 4

2 2

2 3

0 0

3 3

3 1

2 1

0 0

2 2

1 0

0.8

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2.0

2.8

2.3

3.0

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1.5

2.0

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2.8

2.5

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1.5

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5 4.7

5 4.7

2 2.0

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2 2.0

4 4.7

2 2.0

4 4.7

1 1.0

3 3.7

2 3.7

3 3.7

4 4.0

2 3.3

3 3.0

4 4.0

5 2.3

Note: Biotope classes 1–17 (columns) are explained in Table 2.

Figure 2.

Research stages.

‘no relevant capacity to provide a certain ES’, and 5 presented a ‘very high-relevant capacity to provide a certain ES’ (see Burkhard et al. 2009, 2011). Experts filled in their estimations in the table by themselves, and for controversial judgements the mean value was used. Based on the expert judgement values, maps of ESs supply were developed (Figure 3). Finally, a synthesis map was produced of the areas where seven or more ESs classes got high-relevance values (4 or 5) (Figure 4). Those polygons were described as multifunctional ESs ‘hot spots’, which should be taken into particular account in management decisions. In addition to individual biotopes’ capacities to provide ESs, their importance for biodiversity and particular endangered species was taken into consideration (see Table 2).

Results Biotope data versus CORINE data The use of the detailed biotope data revealed differences in landscapes’ capacities to provide ESs. The biotope data especially enhanced the evaluation of forested areas. In the CORINE land-cover data, there are only classes for coniferous, deciduous and mixed forests, while in the detailed biotope data, age structure, canopy coverage and ground vegetation types can also be identified. We considered different options, but we think that age structure in these northern forests is the most important parameter. Thus, we only used the age data for reclassification in this article. This improves the evaluation of many forest-based ESs (Figure 3). For mires and peatlands, the use of biotope data

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Figure 3. Maps of capacities to provide selected ecosystem services using detailed biotope data (upper maps) and CORINE land-cover data (lower maps): (a) energy; (b) game; (c) recreation; (d) reindeer; (e) berries and fungi and (f) water.

also offers more detailed classifications than CORINE. To assess the other biotope types, the detailed biotope data can offer a lot of potential for linking biodiversity data with land-cover characteristics, but we did not evaluate those classes comprehensively, because we focused on the forest classifications. The evaluation of the biotopes’ associated biodiversity, such as endangered species, cannot be done exhaustively from coarse land-cover data. Here, the detailed biotope data provide better information about the vegetation

types and their proportional coverage in the study area (Table 2). The data offer better opportunities to evaluate the sites’ importance for particular species, especially for vascular plants and other sessile organisms. Larger animals, such as wolverine and golden eagle, are more dependent on the landscape structure and wider biotope entities, even though some local biophysical characteristics, such as caves or old trees, or special biotopes may have importance for some aspects of their life, e.g. nesting.

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Figure 3.

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(Continued).

Integrating varying ESs and biodiversity The biotope classes old pine forests, old broad-leaved deciduous forests and old mixed forests obtained high values (4–5) for nine ESs; mires and peatlands with forests/bushes had high values for eight ESs and lakes and rivers showed high potentials for the provision of seven ESs (Table 3, Figures 4 and 5). The comparison of the main ESs groups shows that cultural ES received, in general, the highest average values. The highest variation between biotopes was found in the group

of regulating ESs. Individual regulating services received high values in special biotopes (such as carbon storage in mires and peatlands), even though the average values were low. Some provisioning services have also local economic relevance, such as games, fishes, berries and fungi, all associated directly with recreation and tourism, while other ESs have national or even global relevance, such as carbon storage. The matrix of expert judgements, on which the ESs mapping was based, is presented in Table 3.

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Figure 3.

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(Continued).

Discussion Suitability of biotope data for ESs mapping Mapping and quantifying ESs supplies are a rapidly evolving research topic (Bolliger and Kienast 2010). Outcomes of the ecosystem assessments are particularly dependent on the methods specified for the scale and regions, as well as the respective society living in the surroundings of the study area, and the quality of the available data or indicator proxies. Coarse environmental data sets may give rough estimations of global status and trends of ESs, such

as the current attempt to produce a global land-cover map of 300 m grid resolution in the GlobCover project (http:// ionia1.esrin.esa.int/). However, in local, regional and even national surveys, more detailed land-cover data are needed, especially when biological composition of ecosystems, such as vegetation type or community structure, is investigated together with ESs. One issue that has not really been addressed yet refers to the questions related to spatial and temporal scales. The mapping approach presented by Burkhard et al. (2011) as a generic conceptual model, can be applied to any scale and would use data appropriate

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Figure 3.

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(Continued).

to the scale of analysis. Nevertheless, resolutions reflecting the characteristics of the given habitats and temporal dynamics have to be chosen. This is the first time when the general biotope data by Tuominen et al. (2001) was tested for ESs mapping in Finland. A similar kind of biotope data are also available from some other European countries, following the EUNIS classification system (Davies and Moss 1997; EEA 2010), in which this kind of approach could be applied. The results indicate that the tested biotope data can improve the ESs evaluation and mapping on a local scale – for

instance, as in our case, in nature protection areas – while the approach could also be extended to regions outside the protected areas. We argue that by using biotope data, the mapping quality can be enhanced, especially in forest ecosystems for which detailed biological and structural characteristics are available. This approach could be improved further to take biodiversity better into account, using additional data layers together with land-cover or biotope databases. In Finland, for instance, the distribution of many rare and uncommon species, which often have a special conservation status, correlate substantially with the

International Journal of Biodiversity Science, Ecosystem Services & Management

Figure 3.

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(Continued).

nutrient gradient. Areas such as fens- and herb-rich forests, which are particularly valuable for biodiversity conservation, could be distinguished better by using a geological mask, for example, of calciferous rocks and soils, together with biotope data. The other available biotope classifications by Airaksinen and Karttunen (2001) and Raunio et al. (2008) would be feasible for local-scale mapping as well, but their usability is limited because of the poor coverage outside the conservation areas. However, the Natura 2000 biotope classification provides a good

description of associated biota existing in each habitat (Airaksinen and Karttunen 2001), which makes it useful for estimating the needs of biodiversity conservation in comparison with optional ESs supply optimization. The most comprehensive list of Finnish biotopes and their status can be found from the assessment of threatened habitat types in Finland (Raunio et al. 2008). That is, in our opinion, the most promising classification also for future studies, when focusing on biodiversity–ES relationships in Finnish and other similar ecosystems. The use of very detailed biotope data is, however, time-consuming, and a

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(Continued).

stratified sampling approach should be developed before the data can be used in wider national or regional ESs assessments. We compared the results of this article with a previous study by Vihervaara et al. (2010), which was conducted partially in the same area, but using CORINE land-cover data only. One benefit of the CORINE classification is its availability and thus, comparability in most European countries. The weak points are vague and rather general land-cover classes for the different forest types and mires, and the lack of mountain-birch vegetation that has

a special importance for ecosystems and many species in Arctic regions (Program for the Conservation of Arctic Flora and Fauna 2001). In particular, the forests, including mountain-birch vegetation, are also the source of multiple ESs in high-latitude areas of generally low biodiversity and climatically limited ecosystem functions and regulating services. Also several provisioning (e.g. firewood, fruits and berries) and cultural services (recreation, spiritual and religious values) are related to northern forests (Matero et al. 2003; Kniivilä et al. 2011). Additionally,

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Figure 4. Overlay analysis map showing the areas of high potential for multiple ES supply, which have to be considered carefully when planning the management of the area. In the background, there is a Landsat TM image dated 2005 (30 m resolution).

Figure 5. An example of fine scale biotope data used for ES evaluation. In the background, there is an aerial photograph dated 2002 (with 50 cm resolution), which has been the basis of habitat digitizing. (c) National Land Survey of Finland licence No. 51/MML/1.

the use of detailed biotope data supports a better linkage of biodiversity information with landscapes’ capacities to provide ESs, supporting our understanding of complex ecosystem function–service relations. The importance of

biodiversity for the supply of ESs has been mentioned regularly (Haines-Young and Potschin 2010). However, a clear cause and effect identification, linking alterations in biodiversity to changes in ES supply, has so far remained

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on a conceptual level mainly (de Groot et al. 2010; Kumar 2010). With regard to landscape management, it is important to know, whether the conservation of biodiversity will also conserve ESs (Egoh et al. 2007). Nowadays, the multi-spectral, high-resolution data acquisition is not necessarily slow or costly anymore. Satellite imageries are available below 0.5 m resolution, and together with LiDAR data (Digital Elevation Models, DEM and Digital Surface Model, DSM) they can be used for forest habitat delineation, for example, delineation of age structures based on tree heights. Landscapes’ capacities to provide ESs have been studied in some other recent studies, using a similar approach as we did (Burkhard et al. 2009, 2011). Nedkov and Burkhard (2011) showed that land-cover-based mapping can be conducted in varying environmental regions. Evaluating the values of different ESs could benefit from an internet-based automated query, which also could use a GIS approach. Maes et al. (2011) draw together the availability of current data considering several ESs in Europe, and used an assessment methodology based on CORINE land-cover data. The comparison of landscapes’ capacities to provide multiple ESs is a key for sustainable landscape planning and management of, for instance, protection areas. Synergies among various stakeholders operating in the region, and the spatial comparison of the ESs supplies on which they are depending, can reveal possible points of conflict beforehand (cf. Vihervaara et al. 2010). This could help to avoid inauspicious land-use planning. Technically, we were encouraged by biotope data for the study area, which is available for research purposes. Challenges of the presented data set in evaluating ESs are the absence of nation-wide, available, high-resolution biotope data, and also the choice of relevant categories for reclassification of multitude biotope classes, to fit into the purposes of certain ES’ assessment. The small number of six experts making their ESs assessments was undoubtedly a source of uncertainty in the results. However, these shortcomings were reasonable, because the main point of this article is not to make an exact ESs assessment for the area, but to further develop the methodology. Additionally, the usefulness of different data sets as a foundation of the ESs mapping was studied. One future challenge of the described methodology is to use a panel of experts and laymen, which should be larger and more diverse that the one we were able to establish for this study. For example, it might be reasonable to ask professionals, primarily, about values for regulating ESs, because related ecosystem functions could be too complex to be understood by non-experts, while the implementation of sustainable planning needs to take into account various stakeholders, and to reach mutual understanding and common acceptance among them. Using expert judgements as a foundation for the ESs assessment is often a rather easy and quick way to gain a good impression and understanding of the studied systems, but semi-quantified, classified evaluations are just a first step towards proper quantified ESs assessments. Even so, accuracy of ESs evaluations based on interval

judgement data could be developed further with uncertainty analysis (Leskinen and Kangas 1998), which has only seldom been included in ESs studies (Seppelt et al. 2011). However, it seems that without taking exact quantified indicators into account for several ESs classes, such as energy, carbon storage and nutrient flows (cf. Nelson et al. 2009), the full potential of the detailed biotope data cannot be used, and even the impact of such surveys stays weak among the decision-makers. Implications for environmental management Current strategies for sustainable landscape management outside the protection areas try to integrate nature conservation and economic development, in order to increase public support for the biodiversity protection, on the one hand, and to increase the societal acceptance of economic activities within the paradigm of sustainable development, on the other hand (Tallis et al. 2008). Highly relevant bundles of ESs may also have high-economic importance; for example, the provision of energy, wood or medicines. This can make them competitive, with highly valued cultural ESs in that region (Vihervaara et al. 2010), together with some provisioning or regulating services, such as fishes, berries and fungi, carbon storage and erosion regulation, which themselves have a lot of synergies together, but which currently do not have such a high-direct economic influence. It is important to take these offsets into consideration, especially in those nature protection areas which have other land-use purposes than strict nature conservation, such as wilderness areas, some national parks, and particularly in ecosystems outside real protected areas; for example, recreational forests in municipalities which are nowadays considered in land-use planning as so called ‘green infrastructure’, one of the six special targets of the new EU 2020 Biodiversity Strategy (EC 2011). It has been shown that competition for natural resources and land use, based on the exploitation of single ESs, can lead to conflicts between stakeholders (Vatn 2008). In the previous study by Vihervaara et al. (2010), cultural ESs, and livelihoods based on them, such as tourism, were observed to pose an opposite – and thus a possible conflicting – interest for more traditional livelihoods, such as forestry that is based on provisioning services, in Finnish Lapland. Our results showed several biotope classes with high capacities to supply ESs, these were old pine-dominated forests, old broad-leaved deciduous forests, old mixed forests and mires and peatlands with forests or bushes. This raises the question: Should the most valuable areas for ESs provision be taken into account as conservation priorities? In the light of the findings of the MA (2005), which showed that over 60% of the world’s ecosystems are used unsustainably or degraded, it is obvious that land-use influences on ecosystems should be considered carefully. Humaninduced land-cover changes can be a primary pressure increasing biodiversity loss. Nature conservation would, however, be the safest option to preserve the ecosystems’

International Journal of Biodiversity Science, Ecosystem Services & Management natural state and safeguard ESs supplies. Of course, this is a rather unrealistic option outside the current network of protected areas. Our case study from sparsely inhabited northern Finland is an extreme case, where probably the majority of ESs are not threatened so far, if compared, for example, with the densely populated surroundings of urban areas, where the human population is posing various pressures for ecosystems, for example, altering ecosystems, introducing alien species and affecting to nitrogen load. Thus, the special value of the UKK National Park and the Sompio Strict Nature Reserve, and all the other protection areas as well, is their intact environment in which it is possible to study ecological interactions and biodiversity impacts on the various biotopes’ capacities to supply ESs. In addition to land-use effects, the impacts of other humaninduced pressures such as climate change should also be studied in the future. Biodiversity forms the foundation of ecosystem functioning, and linking biodiversity with ESs is a key issue in today’s environmental science (Kumar 2010). Halting biodiversity loss is highly noted on policy agendas (e.g. the EU Biodiversity Strategy to 2020, EC 2011). However, acting towards these goals has to take place mainly at the local level. Thus, means of integrating biodiversity conservation with land-use planning and environmental management are urgently needed (Haines-Young 2010). We assume that one choice to achieve this aim could be applying more detailed biotope classifications as the base of local ESs assessments, as we have illustrated in this article. Biotope classifications consist of better-elaborated data about species composition and ecosystem characteristics than, for instance, previous land-cover-based evaluations described by Maes et al. (2011) and Burkhard et al. (2009), which can improve the understanding of landscapes’ biological functions. Also judicious assumptions about the distributions of threatened species can be done based on detailed biotope classes. Extensive ground surveys of several taxa, such as vascular plants, mosses, birds, butterflies and individual endangered species, have been done, especially in nature protection areas in Finland. These data sets can help recognize linkages between biodiversity and ESs. The ESs assessment based on detailed biotope data can be used also for management planning, such as visitor guidance, restrictions or restoration of particular sites in protected areas, for instance, in the UKK National Park, where plenty of visitors go each year. Finnish national parks and wilderness areas are open to the public and thus are of national importance for recreation. Their role in supporting tourism and thereby local economy is significant (Saarinen 2002). This can lead to situations, where in some areas the growing human pressure on nature decreases the natural state of ecosystems; for example, on some trails close to the Saariselkä area in our case study. However, to test these specific effects, an ESs risk or vulnerability study would be needed. ESs assessment could offer tools to take multiple land-use forms and ecosystems’ capacities better into account. Useful methodologies to enhance

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the management of ESs can be found from optimization methods, such as multi-criteria analysis, studied with the multiple use of forests (e.g. Kangas et al. 2000; Leskinen and Kangas 2005), and from modelling techniques developed for optimizing conservation priorities on the basis of protection area network connectivity and habitat quality estimates, for example, with Zonation software (Moilanen et al. 2005; Lehtomäki et al. 2009). Zonation offers an example of an approach that could support the use of biotope classification data sets in ESs management. Conclusions In this article, we have presented the use of detailed biotope data for mapping ESs supply in Finland, using northern protection areas as an example. This approach could be adopted all over Europe, owing to the availability of similar kinds of detailed biotope databases (e.g. EUNIS). Three results are summarized: first, the ESs mapping approach and methodology based on detailed biotope data sets can be used for comparing distribution of biodiversity with ESs supplies in areas where these data are available; second, even though the main point of this article was not to make a full ESs assessment, the results showed that some biotopes, especially old growth forests, had high relevance for supplying several ESs (at least 7 out of 16 in our case) and third, the biotopes providing many ESs should be considered carefully in management planning, on the one hand, because of their safeguarding role for ecosystem functions, and on the other hand, possible sources of conflict caused by land-use decisions. In future, the improvement of the sensitivity of the described methodology with more detailed biodiversity data and indicator proxies, as well as quantifying case-tailored ESs indicators, are the two main elements towards high-quality ESs assessment. Applying the methodologies, such as multi-criteria analysis, tested with the multiple use of forests, or spatial modelling of conservation prioritization, are possible future paths to widen the scope of ESs research. Acknowledgements We thank the Finnish Forest and Park Service, Metsähallitus, for the kind provision of biotope data for this study. The project has been supported by funding from the Academy of Finland and the German DAAD for researcher exchange project (2009–2011), ‘Managing of Ecosystem Services in Northern Finland’, and the Academy of Finland for REGSUS project. We thank two anonymous reviewers for their valuable comments.

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