Spatial information in ecosystem service assessment: data applicability in the cascade model context

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Spatial information in ecosystem service assessment: data applicability in the cascade model context a

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Harri Tolvanen , Mia Rönkä , Petteri Vihervaara , Matti d

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Kamppinen , Céline Arzel , Nina Aarras & Sirpa Thessler

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Department of Geography and Geology, University of Turku, Turku, Finland b

Department of Biology, University of Turku, Turku, Finland

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Finnish Environment Institute SYKE, Joensuu, Finland

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School of History, Cultural Research and Art Studies, University of Turku, Turku, Finland e

Finland Futures Research Centre, University of Turku, Turku, Finland f

LYNET/MTT Agrifood Research Finland, Helsinki, Finland Published online: 27 Aug 2014.

To cite this article: Harri Tolvanen, Mia Rönkä, Petteri Vihervaara, Matti Kamppinen, Céline Arzel, Nina Aarras & Sirpa Thessler (2014): Spatial information in ecosystem service assessment: data applicability in the cascade model context, Journal of Land Use Science, DOI: 10.1080/1747423X.2014.947642 To link to this article: http://dx.doi.org/10.1080/1747423X.2014.947642

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Journal of Land Use Science, 2014 http://dx.doi.org/10.1080/1747423X.2014.947642

Spatial information in ecosystem service assessment: data applicability in the cascade model context Harri Tolvanena*, Mia Rönkäb, Petteri Vihervaarac, Matti Kamppinend, Céline Arzelb, Nina Aarrase, and Sirpa Thesslerf

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Department of Geography and Geology, University of Turku, Turku, Finland; bDepartment of Biology, University of Turku, Turku, Finland; cFinnish Environment Institute SYKE, Joensuu, Finland; dSchool of History, Cultural Research and Art Studies, University of Turku, Turku, Finland; eFinland Futures Research Centre, University of Turku, Turku, Finland; fLYNET/MTT Agrifood Research Finland, Helsinki, Finland (Received 12 December 2013; final version received 14 July 2014) Spatial information and geographical information systems (GISs) are widely used in ecosystem service research, but both the information and the methods need to be properly understood in order to make coherent analyses. We discuss the practical challenges of incorporating spatial data to ecosystem service assessment in an agricultural landscape and apply the ecosystem service cascade model to put different data into context. We review the prerequisites and practices for successful ‘ecosystem service GIS’ and provide a structured view of the information and data needed in the assessment of ecosystem services at a regional scale. Due to the heterogeneity of the spatial data, the regional characteristics should be considered in environmental decision-making through ethnographic research on local expertise to make optimal choices in using spatial information. Keywords: biodiversity; ecosystem services; environmental monitoring; environmental policy; GIS; sustainability

1. Introduction Ecosystem services are defined as flows of material, energy and information, which are created by natural processes and produce human welfare (Costanza et al., 1997; Millennium Ecosystem Assessment, 2005). Ecosystem services cannot be produced artificially (Nelson et al., 1993), but human activities can either boost (Samnegård, Persson, & Smith, 2011) or suppress (Compton et al., 2011; Zang, Wu, Liu, & Na, 2011) them, and they may be produced in anthropogenic environments, such as urban areas (Lundholm & Richardson, 2010) or artificial wetlands (Murray & Hamilton, 2010). Ecosystem service research should address the various stages of ecosystem service provisioning, e.g. via the so-called ecosystem service cascade model (Haines-Young & Potschin, 2010; Maes et al., 2012). The concept of ecosystem services can also be used to put a price tag on nature (Costanza et al., 1997; UNEP, 2010). So far, the monetary value of ecosystem services has largely been excluded from the global and national economies (Millennium Ecosystem Assessment, 2005; Watanabe & Ortega, 2011). As the use of natural resources exceeds all *Corresponding author. Email: [email protected] © 2014 Taylor & Francis

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sustainability, it is essential to valuate the impacts of the global market economy on nature (Bartelmus, 2009) and integrate economies to the biophysical planetary boundaries. Geographical information systems (GISs) and spatial analyses offer a suitable methodological basis for ecosystem service research, as they provide efficient methods to organise, assimilate and analyse spatial information. GISs have the capacity to handle the complexity of spatial dimensions on different scales and allow the integration of field survey data into models within the same graphic environment (Grêt-Regamey, Bebi, Bishop, & Schmid, 2008) for analysing their spatial relationships. By using GISs, ecosystem service indicators can be computed for multiple scales at any administrative level. It is also possible to compare administrative units of different sizes, because most indicators are area independent. Nevertheless, the spatial scale might vary according to the type of information. The ultimate goal of ecosystem service assessment and management is the sustainable use of ecosystem services on local, regional, national and global scales. Sustainability builds on three dimensions: ecological (the physical environment with its abiotic and biotic processes), economical (local and global economies) and social (cultural resources). When studying ecosystem services as a whole, all three dimensions have to be included (Bunch, Morrison, Parkes, & Venema, 2011; Cabezas, Pawlowski, Mayer, & Hoagland, 2004; Robards, Schoon, Meek, & Engle, 2011; Skourtos, Kontogianni, & Harrison, 2010). Ecosystem services are tightly connected to land-use and land cover change, the analysis of which calls for an integrated view of social, demographic, economic and ecological processes (Luus, Robinson, & Deadman, 2013). The quickly growing field of land-use science (Müller & Munroe, 2014) develops in close interaction with the biodiversity and ecosystem service research. The ecological dimension of sustainability is often treated by the means of natural science, and it can fairly easily be translated to quantified data, such as water quality variables (Usaquén Perilla, García Gómez, García Gómez, Álvarez Díaz, & Revilla Cortezón, 2012). The economical dimension deals with monetary value quantified, to a degree, by market values, but to a large extent, based on abstract valuations (Costanza et al., 1997; Kontoleon, Pascual, & Swanson, 2007). Analysis of non-quantified information in GIS is problematic. Due to the lack of adequate data and methods, there has been a need to settle for less accurate valuation methods for cultural ecosystem services in particular (Chen, Li, & Wang, 2009). For example, the recreational potential has, however, been addressed in GISs using quantified data, such as land cover databases (Maes et al., 2011). Due to the multidimensionality of ecosystem services, the pieces of information about them are diverse and variable, and thus seldom commensurable. This is apparent when knowledge is converted into data for further analysis. As a whole, there are many issues to be addressed concerning the amount, quality, accuracy and reliability of the data. On top of the ecological, economical and social dimensions, the local, national and global controlling measures add layers of complexity. In this study, we review the requirements for the use of GISs and spatial data in ecosystem service assessment in an agricultural landscape at a local scale and structure the role of different data through the ecosystem service cascade model. This process is referred to as ‘ecosystem service GIS’, highlighting the methodological considerations specific for the challenging data diversity, characteristic of the multidisciplinary theme. Furthermore, we describe the sources, quality and interoperability of data and information, using examples from our study area in southern Finland, and discuss their potential for ecosystem service research and management, together with policy implications.

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2. Setting the scene for ecosystem service GIS 2.1. A structured approach to spatial data containing ecosystem service information As ecosystem service research is a broad and complex multidisciplinary field of study, its information sources range from quantitative measurements to knowledge about legislation and folklore. The variety hampers attempts to combine the information, especially to harmonize it to quantified spatial data. Also, in many cases, the accuracy and level of detail in the information content, as well as the spatial and temporal scale, vary. A structured view to spatial data containing ecosystem service information illustrates the variation in terms of spatial data management (Table 1). Any information

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Table 1. A structured approach to spatial data related to ecosystem services, with considerations of different aspects of information in the context of GISs (modified after Tolvanen & Kalliola, 2008). Characteristic Information type Data producer Motivation of collection Type of register Quality of metadata Availability of data Attribute data Use and update status Temporal coverage Geographical coverage Scale Comparability Date of information Thematic environment Thematic range Data model Spatial coverage Spatial consistency Spatial accuracy Datum and coordinates Georeferencing

Range Quantitative ↔ qualitative Public ↔ private

Challenge for GIS analysis More GIS tools available for quantitative analysis Possible effect on data standards and availability Data interoperability issues

Standard monitoring ↔ specific campaign Spatial layer ↔ non-geocoded database Harmonization challenge Good and available ↔ non-existing Crucial for evaluation of applicability Publicly available ↔ restricted

Economical, political or administrative limitations Labels of objects ↔ extensive database Depth of data content affects applicability In active use ↔ passive record Effect on usability, passive data may be reactivated Time snapshot ↔ multi-temporal/long Temporal coverage vital for ecosystem term service study Local ↔ global Affects usability, scale and accuracy issues Small scale ↔ large scale Lack of spatial accuracy/detail may limit use Unique creation ↔ standard data type Uniqueness may hinder comparisons Up to date ↔ outdated Evaluation of usability Terrestrial ↔ aquatic

Individual objects ↔ continuous surfaces Uneven ↔ consistent

Data origins often considerably different Multi-thematic maps often offer less detail Construction of GIS models, spatial accuracy Data origin essential, different applicability Spatial consistency optimal but rare

Detailed ↔ generalized Local ↔ global

Must be evaluated based on purpose Compatibility may require conversions

Coordinate-based ↔ relational spatial labels

Georeferencing may be needed

Subject specific ↔ many themes covered Vector ↔ raster

Note: The range indicates examples of extremes in a dichotomic or multi-value set of characteristics.

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content has a set of characteristics, all of which have a range of possible values. These characteristics must be reviewed each time any information is to be used, since they have a profound impact on the interpretation of the results. The metadata are crucially important.

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2.2. Resources needed for collecting data for ecosystem service GIS Even though the GIS analyses considering ecosystem services will inevitably be more or less short of information, it is essential to understand those data that are included. Expertise is required to make a review of available information, assess its properties according to the characteristics described in Table 1 and implement the information as spatial data for geographical analysis. The complexity of the issue prompts the use of expert panels to establish a common, interdisciplinary and cross-sectoral understanding of the environment and the available information (cf. Vihervaara, Marjokorpi, Kumpula, Walls, & Kamppinen, 2012). Besides human resources, i.e. ecosystem service expertise, the acquisition of spatial data, both existing and new, requires institutional and technical resources, as well as understanding of the local circumstances. As a GIS analysis of ecosystem services is often a local endeavour, local knowledge plays an important role in the process. One needs to be familiar with the environmental as well as administrative and social contexts of the particular region. It is important to know who the stakeholders and data producers are in order to make informed queries for applicable data. The availability of research and monitoring data has improved significantly over the recent years, especially in the environmental sector. For example, in Finland, most of the data produced by public sector offices were, until recently, poorly available outside the particular organization. Even intra-organizational data sharing, for instance between the different administrative sectors of a city, has occasionally been difficult, even involving fees between public sector organizations. Nowadays, data sharing is more efficient, and most public sector organizations follow an open data policy, providing online access to their data. However, concerning data collected or produced in projects, universities, private companies and NGOs, the open data policy is not that apparent. In many cases, the data are produced for one-time need only, and there is no intention, or even a mechanism, to make the information available to others. Also the policies of research grants may restrict data management. The European Union INSPIRE Directive to establish an infrastructure for spatial information in the European Community (EU, 2007) provides useful guidelines not only for the public sector offices that the legislation is aimed at but also for other environmental data producers. In many cases, there is a need to complement the existing ecosystem service information by gathering primary data (Table 2). Nationally invented biodiversity indicators for the farmlands in Finland are closely related not only to the structure and function of ecosystems, but also to the benefits obtained from them (Table 1). Primary data collection requires variable resources, ranging from demanding, extensive in situ inventories to literature reviews and stakeholder interviews. 2.3. Application in southern Finland: case study area and data We use the semi-agricultural drainage basin of the Karjaanjoki River in southern Finland (60°20′N, 24°00′E) (Figure 1a) as a case study area, where we draw examples from. The total area is 2046 km2; it includes 815 lakes, of which 57 cover more than 0.5 km2 each

Journal of Land Use Science Table 2.

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Biodiversity-related indicators for Finnish farmlands (after Luonnontila, 2012).

Pressure indicators Active farms and arable area* Livestock and livestock farms* Fertilizer use** Pesticide use**

State indicators Field clearance and afforestation* Field margins and buffer strips* Traditional rural biotopes*(**) Farmland birds*

Impact indicators

Response indicators

Red-listed farmland species*(**) Directive farmland species*(**) Red-listed farmland habitat types** Directive farmland habitat types**

Management of traditional rural biotopes*(**) Organic farming* Agri-environmental support scheme*

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Farmland butterflies** Weeds on spring cereal fields** Note: Spatial data available (*) or could be collected (**).

(Teräsvuori, 2003). The catchment area is located at the transition between the boreal and hemi-boreal vegetation zones. Its land cover structure is typical of southern Finland, i.e. a mosaic of forest (63 %), agricultural areas (17.7 %), wetlands (12.2 %) and settlements (Kotamäki et al., 2009; Teräsvuori, 2003) (Figure 1b). The ecosystem services provided by the Karjaanjoki catchment area include services connected to agriculture and forestry and recreational services, such as fishing (Marttinen, 2004), waterfowl hunting and birdwatching, as well as swimming and other water sports (Klemola, 2003). The local inhabitants have access to all these services, while visitors use the area in particular for recreation (Klemola, 2003). We identified and collected three types of source data: (1) topographic and environmental data from national databases, (2) locally available environmental data and (3) inventory data collected for this study (Table 3). The first source comprises of spatial data that are available throughout the country but may, however, have differences in the quality and uniformity of the spatial coverage or be spatially biased. The second and third sources include additional data on this specific region. Spatial data from site-specific sources comprise mainly of the data of the SoilWeather monitoring network, established during 2007–2008 and coordinated by the MTT Agrifood Research Finland (Kotamäki et al., 2009). The network includes 70 sampling sites (Figure 1c); these host a total of 55 weather stations, 30 soil moisture probes and 22 water quality probes. The probes are located mainly in fields owned by private farmers. Of the water quality probes, 18 measure water turbidity, and four measure the nitrate content. The monitoring stations submit their data almost in real time, enabling the development of innovative information services for local farmers and researchers. The accumulating database also provides research data for studies focusing on microclimate and local hydrology. Three types of primary data were collected. First, we carried out a field study to collect data about the biodiversity of 11 lakes that represent different environmental settings (see Figure 1c). The field study was conducted in the summer 2010 and included an inventory of the waterfowl and night-singing birds according to the methods of Koskimies and Väisänen (1991). Bird inventories were used as an additional source of environmental data, because birds are well-known indicators of the environment and its

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Figure 1. (a) The drainage basin of the river Karjaanjoki in southern Finland. (b) Protected areas and sub-catchments in the study area. (c) Locations of the 70 SoilWeather network sites and the 11 lakes where the biodiversity censuses were carried out.

ecological quality (Austin, Buhl, Guntenspergen, Norling, & Sklebar, 2001; Bennun & Fanshawe, 1997; Bibby et al., 1992; Donald, Green, & Heath, 2001; Gregory et al., 2003; Pearson, 1995). Second, we inventoried the potential food resources of waterfowl, i.e. invertebrates and seeds in the water column, and shoreline vegetation (Elmberg, Nummi, Pöysä, & Sjöberg, 1992; Murkin, Abbott, & Kadlec, 1983). These field data provide information on the quality of the breeding habitats for waterfowl (Arzel et al., 2007, 2009). Third, we conducted a survey on the 39 farmers that participated in the SoilWeather network (Rönkä et al., 2013). The survey was conducted during 2010– 2011, and it consisted of a questionnaire and an interview study.

Journal of Land Use Science Table 3.

Summary of main data sources.

Source type

Data

Source

Notes

National database

Terrain topography Watershed limits Agricultural parcels Surface water properties Hydrological monitoring CORINE land cover Biodiversity inventories Diverse research data Diverse research data SoilWeather

National Land Survey Finnish Environment Institute Statistics Finland Finnish Environment Institute Finnish Environment Institute European Environment Agency Finnish Environment Institute Various institutes Various institutes MTT Agrifood Research Finland Local municipalities Own waterfowl inventories Own invertebrate inventories Own survey and interview

Uniform national coverage Uniform national coverage Uniform national coverage Uniform national coverage Uniform national coverage Uniform national coverage Variable national coverage Various coverages Various coverages Only in this area

Local database

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Primary data

Municipality data Biodiversity Biodiversity Agricultural practices

Only Only Only Only

in in in in

this this this this

area area area area

3. Making it work: applicability and harmonization of data 3.1. Spatial data in the cascade model context We applied the ecosystem service cascade model (Haines-Young & Potschin, 2010; Maes et al., 2012) to characterize the availability and applicability of spatial data in our study area (Figure 2). The cascade model is a useful concept to frame spatially explicit, quantitative assessments of ecosystems, as well as ecosystem services and their benefits. This framework links biodiversity and ecosystems stepwise to human well-being through the flow of ecosystem services (Maes et al., 2012). We use the cascade model in particular to illustrate that spatial data are applicable in all steps of the process flow in the cascade model context (Figure 2), highlighting the importance of coherent information extraction from the source data-sets. The use of the cascade model also addresses issues concerning data availability, applicability and interoperability in the ecosystem service context. Prior to environmental GIS analyses, the problems related to technical, administrative and content interoperability need be addressed and solved, in order to extract relevant and correct information from the data.

Figure 2. The source data types (see Table 1) allocated to the different steps of the ecosystem service cascade model (see Haines-Young & Potschin, 2010; Maes et al., 2012).

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3.2. Aspects of data scale and accuracy Technically, the data concerning any environmental or socio-economical feature should be presented on a geographical scale that is feasible for the particular phenomenon, purpose of analysis and area unit. Existing databases offer useful basic data on ecosystem services on many spatial scales (Figure 3). Still, the decision upon the suitable scale for each dataset remains with the user. More importantly, one data-set rarely represents one ecosystem service. Therefore, even more discretion about scales is required when examining combinations of data-sets. Environmental and especially socio-economical objects are complex spatial features, and often transitional without indisputable limits. Ideally, their spatial representation should therefore also involve neighbourhood and network dimensions, which describe the spatial dependency between the individual objects (Tolvanen & Kalliola, 2008). When searching or producing data for ecosystem service GIS, the data should be assessed in the context of the particular purpose. Turning real-world phenomena into spatial data involves conceptualization of the environment and its elements to enable their numerical representation, analyses and visualization. A thematically, spatially and temporally precise digital model that embraces all aspects of the environment and human impact would be ideal, but all digital representations about reality are inevitably simplified (Tolvanen & Kalliola, 2008). Therefore, the representativeness of any data-set should be carefully reviewed.

3.3. Demand for reliable local information In addition to being spatially and thematically accurate, information used in ecosystem service GIS should be administratively coherent, i.e. follow harmonized practices in their production processes. The administrative background of data-sets may affect their applicability. Public or private sector organizations produce and maintain data primarily for their own purposes, which might lead to inconsistency in data harmonization and interoperability. Administrative incompatibility may stem from the traditional division between the management of land and water areas or issues related to geographical dimensions and borders: for instance, economic areas are usually defined by administrative boundaries, ecological areas have natural boundaries with transitional zones and human-induced environmental impacts extend beyond natural and political boundaries (Westmacott, 2001). Knowledge of the history of data collection, e.g. aims, sampling and analysis, is a prerequisite to using the data correctly and making accurate conclusions. Awareness of the origin of data is even more important now that governmental institutes and other data producers are sharing their data. In the same time, new automatic technologies, such as the SoilWeather network, and crowdsourcing are increasingly used in data collection. These novel data collection methods require more efforts in data management and data quality assessment, but can also be a cost-effective way to complement current monitoring systems and to get feedback on data quality. The cascade model approach reveals challenges related to the scale and administrative sources of data. There are fewer data available for the assessment of human well-being than for the analysis of physical, chemical and ecological ecosystem properties. Several quantitative data-sets on environmental features can be applied to the assessment of human well-being, but tools would be needed in particular to assign socio-economic features to the same spatial units as ecological features. Our assessment also highlights the locality of the data needed for certain aspects of ecosystem service assessment. While the

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Figure 3. Examples of spatial information related to ecosystem services. (a) Sample area location. (b) Elevation contours. (c) Raster elevation model derived from the contours. (d) Slope angle. (e) Infrastructure (roads, buildings, SoilWeather sites). (f) CORINE land-use classification. (g) Agricultural parcels classified by area. (h) Agricultural parcels classified by mean slope angle.

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general structure of ecosystem services in a specific region can be traced using national databases, the assessment of particular ecosystem processes and values related to ecosystem services rely on local data.

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4. Discussion 4.1. Methodological considerations It is commonly stated that biodiversity is underpinning the provision of ecosystem services (EASAC, 2009; Haines-Young & Potschin, 2010; Millennium Ecosystem Assessment, 2005). Integration of biodiversity conservation and sustainable use of ecosystem services is a key target of international policies, such as EU Biodiversity Strategy for 2020 and the Aichi targets of the Convention on Biological Diversity (CBD). However, the interdependencies between biodiversity and ecosystem services are far from comprehensively studied (Anderson et al., 2009). Monitoring changes in the state of biodiversity and ecosystem services is based on indicators (Normander et al., 2012), but, until present, the integration of biodiversity indicators has not been very common at the local level (Tasser, Sternbach, & Tappeiner, 2008). The existing records of environmental observations give a good overview of the global environment (e.g. Secretariat of the Convention on Biological Diversity, 2010), but only few data (e.g. the European Long-Term Ecosystem Research Network) are applicable for the inventory and valuation of the local environment and ecosystem services. Changes in the quantity of ecosystem services at the local level could be estimated for instance by analysing land-use and land cover changes from satellite data (Zhao et al., 2004). The development of indicators for measuring ecosystem services often requires spatial data. The scale at which ecosystem services are measured depends on the content of decision-making (UNEP-WCMC, 2011). For instance, at the local scale, such as in our case study area, detailed biodiversity inventory and field parcel data may be used in relation to the interviews of the farmers, who are the final decision-makers concerning the management of their fields. Rising to the national scale decision-making, more general spatial data, such as CORINE land cover data, set together with topographical models, are applicable. Ecosystem service GIS relies largely on deriving additional information from data-sets by analysing them together. However, issues concerning technical and semantic interoperability of the data-sets present conceptual and practical challenges (Tolvanen & Kalliola, 2008). When information is combined, harmonized or compared across regional or national borders, commonly agreed principles towards spatially explicit semantics and ontology are needed (Tolvanen & Kalliola, 2008). Still, it may not be feasible to apply equally detailed criteria in all areas. Standard spatial data layers from alternative sources may also reveal a high degree of redundancy, which may force the user to select one of the alternative data-sets (Tolvanen & Kalliola, 2008). In ecosystem service GIS, both spatial and temporal scales have to be managed. The spatiality itself raises methodological considerations about the spatial coverage, representativeness and accuracy of the data. Also the temporal change is often problematic, since temporal stationarity is uncommon in many land-use systems (Bakker & Veldkamp, 2012), but long-term time series data are rare. The overall shortage of high-quality environmental and other ecosystem service data calls for methodological interoperability optimization to avoid unnecessary exclusion of data due to, for instance, scale-related issues. Data on biodiversity often lack uniform national coverage or concern only specific

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locations. Furthermore, such data, often collected for specific research projects, tend to be short term. This hampers, in particular, the assessment of processes concerning ecosystems and biodiversity, which would require long-term data. The social dimensions of ecosystem services, often underrepresented in spatial analyses, could be assessed by ethnographic community research, which provides understanding of the sociocultural boundary conditions of ecosystem services: what are the life styles, values and future aspirations of the regional communities (Riner, 1987). Ethnographic community research assesses the sociocultural dimensions of ecological facts and processes – how, for example, the diversity of birdlife is appreciated. Furthermore, ethnographic community research can offer heuristic tools for designing the details of ecosystem research, including the collection and utilization of spatial information. If, for example, the community research brings about the importance of transport and roads, then the ecosystemic relevance of these constructions can be taken into account in research design (Atran, Medin, & Ross, 2005; Hess, 1999). The multidimensionality of information about ecosystem services creates challenges for research and development. The multiple aspects of the study object can be conceptualized by means of systems thinking and its central tools of thought, such as level and emergence (Bunge, 1985; Kamppinen, Vihervaara, & Aarras, 2008). In systems thinking, the world is seen as composed of systems, and all entities populating the reality as linked with other entities. The level can be defined as a collection of similar entities, such as plant and animal species and other ecosystem components. As we proceed from one level to a higher one, e.g. from ecosystem components to entire ecosystems or from ecosystems to social-ecological systems, new properties emerge (Kamppinen et al., 2008). For example, new economic, social or cultural values may emerge from biodiversity on ecosystem level, or from ecosystems on the level of social-ecological systems (Kamppinen et al., 2008). 4.2. What are we currently hitting and missing with ecosystem service GIS? Ecosystem service research with its wide information requirement range poses new challenges for the environmental GIS. The large variety of data types and sources demands new methodological approaches. The applicability of current environmental data archives should be maintained in the context of ecosystem services, while many additional information sources need to be utilized. Interlinking biodiversity protection and the restoration of ecosystem services is one of the most urgent needs concerning the development of mapping methods. Individual datasets or models rarely show the linkage between biodiversity and ecosystem services. The enhanced use of habitat databases, such as the European Nature Information System’s (EUNIS) classification (Davies, Moss, & Hill, 2004), and applied remote sensing methodologies together with traditional ecosystem service data (Vihervaara, Kumpula, Ruokolainen, Tanskanen, & Burkhard, 2012), would link biodiversity more closely to ecosystem services. Human activity involves an unending variety of potentially relevant, spatially distinctive themes, ranging from the environmental pressures posed by different economic activities to aesthetic values that may have geographical connotations. These largely qualitative features, however, are often problematic to include in GISs (Dyer & Millard, 2002). In our case study, the farmer interviews serve as an example of non-quantified socio-economical data, which determine the ecosystem service cascade stages related to human well-being. Quantifying opinions is ambiguous and in many cases not even

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relevant. In addition, they are hard to tie to a specific location, even though in some cases a specific site or region for an activity can be identified. Some socio-economic features, such as employment and income, are easier to quantify, but also then the challenge remains to find a uniform resolution and scale with ecological features. Furthermore, quantification of human perception, judgment and decision-making, and their rationality, is far from straightforward (Meyfroidt, 2013). While the location of industries, road networks and agricultural parcels has clear spatial properties, the assessment of their environmental significance requires attribute data about their use volumes, impact areas or prognoses of their future use and development (Tolvanen & Kalliola, 2008). However, even the quantified environmental data require careful processing, since data that appear coherent and commensurate may cause problems because of the complexity of their content. Overall, the complexity of the environment, human activity, their interaction and especially the data that describe them is overwhelming. In some cases, a single ecosystem service is the product of two or more ecosystem functions, whereas in other cases a single ecosystem function contributes to two or more ecosystem services (Costanza et al., 1997). Thus, the representation of these functions and services as GIS layers is challenging. There is a need for methodical development that appreciates the complexity when drastic simplifications of the interconnected processes related to ecosystem services are made. Ecosystem complexity alone challenges us to discuss the limitations of scientific methods (Loehle, 2011). Our case study area is exceptional in terms of the good availability of detailed hydrological and meteorological observations through the SoilWeather network. In other areas in Finland, the weather observation data are available only for a sparse observation network. Also hydrological data are usually limited. While flow rates are often monitored, water quality data remain uncommon (e.g. Brauman, Daily, Duarte, & Mooney, 2007). Water quality models have been developed (Maes et al., 2012), but differences between the models can be substantial. 4.3. Policy implications The term ecosystem service is being introduced to concrete use in decision-making, as new directing and controlling measures are developed for environmental management, including land-use planning. Knowledge about ecosystems is utilized for the purposes of regional management, for creating better futures (Shrader-Frechette, 1994; ShraderFrechette & McCoy, 1993). Simultaneously, the focus is shifting from single natural resources into larger entities, also in the assessment of the economical value of the environment. For instance, Chen et al. (2009) assessed the economic value of a combination of ecosystem services at a county scale. In spatial terms, the concepts of ecosystem service formation and management decision impact may occur on very different scales. For instance, an individual farm is a common unit of management decisions in most issues regarding the agricultural environments. However, a farm covers usually only a fraction of the area that could be considered a unit where ecosystem services are formed, such as a local drainage basin. This discrepancy is case dependent, and its effect upon ecosystem service research is variable. Even though the mere need and relevance of the economic valuation of ecosystem services have been criticized (Spash, 2008), it is clear that their value will be incorporated in the economic system, in one way or another. For this purpose, ecosystem services must first be inventoried in sufficient detail to understand their functions and interdependencies.

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A valuation derived from literature, and based on average hectare value for a given land cover type, has been used in ecosystem GIS studies (e.g. Troy & Wilson, 2006), but the results can only be as good as the source data allow. Local data provided by ethnographic community research could help in the valuation, if tools are found to harmonize such data with data presenting other aspects of ecosystems. In the area of applied research and especially development, the decision-maker has to decide which levels and future paths are relevant. Decision-making requires structured options and choices concerning the nature and relevance of different dimensions: ecological, economical and sociocultural. The temporal and spatial scales add to the complexity. The decision-maker faces the dilemma of optimal decision: the more considerations are taken into account, the more complicated it is to find a common measure for different consequences (Kamppinen & Walls, 1999). Decision-making involves three steps: identification of alternatives, identification of impacts and valuation (Daily et al., 2000). Enhanced technologies for collecting and processing spatial data provide better grounds for balanced decision-making, but as they bring in more information, their use should be constrained by ethnographic community research. The information on ecosystem services should be approached in steps: once a threshold of enough information is achieved, syntheses are possible. Ecosystem service GIS has high data demands in order to produce as comprehensive results as possible. However, the results should be simplified for decision-makers to a few, but significant indicators (Tasser et al., 2008). Data reduction is a feasible feature already when gathering information for the ecosystem service GIS. For instance, it may be possible to choose the respective indicator, with the highest loading on each factor as determined by a factor analysis (Riitters et al., 1995). Sustainability of a system is characterised by the co-evolution of social, economic and environmental systems, and the organization of these systems, called the institutional or political system, which includes the regulation of the economic and social systems and the relations with the environmental system (Graymore, Wallis, & Richards, 2009; O’Connor, 2006). A conceptual framework for common local sustainability indicators allows the assessment of common resources managed at regional level. Benchmarking among municipalities within a region and coordination of efforts at the local level enhance the monitoring of local and regional sustainability (Mascarenhas, Coelho, Subtil, & Ramos, 2010). In order to ensure the sustainable use and management of ecosystem services, it is essential to create participatory integrated ecosystem service valuation processes (Fontaine et al., 2014) and launch sustainability monitoring systems with integrated biodiversity indicators on both regional and local scales (Tasser et al., 2008). Rather than by building new monitoring schemes, such systems could be implemented by a more cost-effective and integrative use of currently collected data and by adopting new monitoring methods and technologies, such as modelling, remote sensing, automated monitoring technologies and crowdsourcing. 5. Conclusions As discussed in this article, the GISs provide a methodological basis for combining and analysing ecosystem service information that is somehow tied to a location. The quality and availability of environmental spatial data relevant to ecosystem service research are improving, and the methods to include qualitative data in GIS are being developed. An urgent future challenge for GIS applications is the expansion of biodiversity indicators to

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cover the full range of ecosystem services, also in terms of different stages of the ecosystem service cascade model. On the basis of our case study, it is recommendable that the use of multiple knowledge bases and all the more sophisticated data collection technologies should be constrained by regional considerations, including ethnographic community research. Recent advances in GIS methodology should be more comprehensively and effectively implicated to illustrate the importance of research results for research–policy interfaces. The utilization of GISs in ecosystem service research is far from comprehensive. This is caused by five main hindrances: (1) The difficulty to bring qualitative socio-economical information into GISs. (2) The limitations of GISs in handling the different information types and temporal dynamics. (3) The overall lack of many crucial data that would be required to gain a full understanding of the ecosystem services and their spatio-temporal extents and impacts. (4) The problems concerning the different spatial scales and accuracies of information. (5) The difficulties in conceptualizing the relationship between biodiversity and ecosystem services. These challenges are best tackled by means of anchoring the use of spatial information to local conditions. This requires the use of ethnographic research on local expertise, as well as other case studies carried out in the target area. Especially in the case of building policy recommendations, the strategic use of spatial information requires a piecemeal, step-by-step approach, where the information is tailored to the tasks at hand. Many ecosystem service valuation methods are incommensurate with each other, e.g. direct market price, replacement cost and contingent valuation methods. However, individual valuation models themselves can bring about commensurability within the specific method. Both in the regional (or case study anchored) use and in the valuation of ecosystem services, the inherent heterogeneity of spatial information is manageable. In the regional study, the information needs are more clearly articulated from the very beginning of the project, and hence the researchers and the decision-makers are able to tailor the types of spatial information they need.

Acknowledgements We would like to thank the MTT Agrifood Research Finland, in particular H. Huitu, for cooperation concerning the SoilWeather data and the field study in the Karjaanjoki River catchment area. The study was conducted as part of a project Regional sustainability – ecosystem services and environmental technology (REGSUS), financed by the Academy of Finland (grant number 131893), and carried out in collaboration with the Finnish Environment Institute, the Academy of Finland project 251806, and with a project entitled Wetland suitability to migratory waterfowl in a changing world, financed by the Kone Foundation.

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