Bundling ecosystem services in Denmark: Trade-offs and synergies in a cultural landscape

Landscape and Urban Planning 125 (2014) 89–104

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Bundling ecosystem services in Denmark: Trade-offs and synergies in a cultural landscape Katrine Grace Turner a,b,∗ , Mette Vestergaard Odgaard a,b , Peder K. Bøcher b , Tommy Dalgaard a , Jens-Christian Svenning b a b

Aarhus University, Department of Agroecology, Blichers Allé 20, PO Box 50, 8830 Tjele, Denmark Aarhus University, Department of Bioscience, Ny Munkegade 116, 8000 Aarhus C, Denmark

h i g h l i g h t s • • • • •

Identification of potential synergies for increasing multifunctionality. Distinct differentiation in the distribution and groupings of ecosystem services in Denmark. Cultural and regulating services segregated from provisioning services in cultural landscapes. Ecosystem service bundle types highlighting the spatial trade-offs and synergies. Coastal areas important for cultural services.

a r t i c l e

i n f o

Article history: Received 13 June 2012 Received in revised form 21 January 2014 Accepted 2 February 2014 Keywords: GIS Multifunctionality Spatial distribution Landscape scale

a b s t r a c t We made a spatial analysis of 11 ecosystem services at a 10 km × 10 km grid scale covering most of Denmark. Our objective was to describe their spatial distribution and interactions and also to analyze whether they formed specific bundle types on a regional scale in the Danish cultural landscape. We found clustered distribution patterns of ecosystem services across the country. There was a significant tendency for trade-offs between on the one hand cultural and regulating services and on the other provisioning services, and we also found the potential of regulating and cultural services to form synergies. We identified six distinct ecosystem service bundle types, indicating multiple interactions at a landscape level. The bundle types showed specialized areas of agricultural production, high provision of cultural services at the coasts, multifunctional mixed-use bundle types around urban areas and forest recreation bundle types with high hunting potential. Thus we found that the distributions were both determined by historical and current socio-ecological influences. This gives a better understanding of the interactions between multiple services in the landscape and the way the landscape has been managed. However, the number, types and spatial distribution of such bundles are quite sensitive to the individual ecosystem services selected and the input data available to define these services. This should be taken into consideration in further research on how to utilize the existing synergies and the mitigating potential of trade-offs for a more holistic approach to landscape-scale ecosystem service management. © 2014 Elsevier B.V. All rights reserved.

1. Introduction

∗ Corresponding author at: Aarhus University, Department of Agroecology, Blichers Allé 20, PO Box 50, 8830 Tjele, Denmark. Tel.: +45 5046 4025. E-mail addresses: [email protected] (K.G. Turner), [email protected] (M.V. Odgaard), [email protected] (P.K. Bøcher), [email protected] (T. Dalgaard), [email protected] (J.-C. Svenning). http://dx.doi.org/10.1016/j.landurbplan.2014.02.007 0169-2046/© 2014 Elsevier B.V. All rights reserved.

Ecosystem services are the goods and services that ecosystems provide to society and may be categorized as provisioning, regulating, cultural, and supporting services (Millennium Ecosystem Assessment [MA], 2005). Wherever humans live, complex socioecological interactions are formed with the surrounding landscape, affecting the availability and usage of ecosystem services. For example, social drivers such as urbanization, agriculture and associated deforestation influence the distribution of ecosystems and their services (Alberti, 2005; Geist & Lambin, 2002; Power, 2010). These

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different uses of the landscape are termed spatial trade-offs or synergies, depending on whether the presence of one service excludes the presence of another service or multiple services are able to coexist in the same area (Rodriguez et al., 2006). Trade-offs between for instance agroecosystems and wetlands and their associated services may cause one service, e.g., provisioning of agricultural products, to damage other services, for example via drinking water pollution, lake eutrophication, or habitat loss (Carpenter et al., 2009; Pretty, 2008). There can also be positive interactions – synergies – between multiple services, such as wild flower patches in agricultural land that increase pollination and yield of crops (Bennett, Peterson, & Gordon, 2009; Raudsepp-Hearne, Peterson, & Bennett, 2010). Consideration of such positive and negative interactions between ecosystem services is thus crucial for landscape planning to avoid costly negative trade-offs and promote multifunctionality (Bennett et al., 2009). Spatial trade-offs and synergies between ecosystem services may cause multiple services to form so-called spatial bundles because of their connectedness or interdependence across a given landscape (Bennett et al., 2009; Raudsepp-Hearne et al., 2010). Importantly, quantification of spatial bundles enables geographic representation and analyses of related ecosystem services without the double-counting that would result if they were treated as unrelated entities (Raudsepp-Hearne et al., 2010; Rodriguez et al., 2006). Such analyses have revealed that the landscape distribution of ecosystem services may correspond to predictable socio-ecological subsystems and associated landuse types, e.g. agriculture, forest recreation, and multifunctional land uses (Dick et al., 2010; Raudsepp-Hearne et al., 2010). Importantly, spatial bundle analyses may capture how naturally occurring ecosystem services are linked with human-controlled land uses and their directly associated services (Bai, Zhuang, Ouyang, Zheng, & Jiang, 2011; Dick et al., 2010; Raudsepp-Hearne et al., 2010). Currently – and even more so in the future – many landscapes will be intensively managed and dominated by strong agricultural and urbanization pressures (Borgström, Elmqvist, Angelstam, & Alfsen-Norodom, 2006; Laterra, Orúe, & Booman, 2012; RaudseppHearne et al., 2010; Uthes et al., 2011). Here, ecosystem services are produced under high human population densities and farming intensity (Carpenter et al., 2009; Ellis, Goldewijk, Siebert, Lightman, & Ramankutty, 2010; MA, 2005). Understanding trade-offs and synergies among ecosystem services in such landscapes dominated by humans are therefore crucial. In the present study we address this issue for Denmark, a densely populated and intensely farmed region (Dalgaard et al., 2007). Our specific study questions were: (1) Are there distinct spatial patterns of ecosystem services across Denmark? (2) Are there specific spatial trade-offs and synergies between ecosystem services? (3) Do multiple ecosystem services consistently coexist and form spatial ecosystem bundles? To answer these questions we used the framework developed by Raudsepp-Hearne et al. (2010) for a Canadian peri-urban agricultural landscape, allowing us to compare the findings of the two studies. This will in the following simply be referred to as the Canadian study. Following the Canadian study, some ecosystem services were represented by land use or land cover data. There has been much debate on the accuracy and use of coarse-scale regional studies with simple land use or land cover data for estimating ecosystem services, as is used with this framework (Bennett et al., 2009; Burkhard, Kroll, Müller, & Windhorst, 2009; Seppelt, Dormann, Eppink, Lautenbach, & Schmidt, 2011). Certain ecosystem services such as agricultural provisioning services are in fact closely tied to specific land uses and associated land covers. As in our case, an important additional reason for choosing land use/land cover data is that this type of information is broadly available and enables data

Fig. 1. Denmark.

comparison between regions (Raudsepp-Hearne et al., 2010). Alternative complex landscape-scale assessments of multiple ecosystem services are often difficult to apply to different landscapes, limiting our ability to compare and generalize (Koschke, Fürst, Frank, & Makeschin, 2012). 2. Materials and methods 2.1. Study area Our study area was the land area of Denmark (43,000 km2 , Fig. 1), excluding the islands of Bornholm, Ertholmene, Læsø, and Anholt. Ertholmene (two islands) were considered too isolated (>100 km from the mainland) to be geographically coherent with the rest of Denmark; the latter two were excluded because of missing data. This resulted in a final study area of 41,965 km2 . The Danish landscape has been formed by erosion and sedimentation from the glacial cover of northern Europe, which is manifest in its low rolling hills and flat glacial deltas (Meesenburg, 1996). It consists of the peninsula Jutland, the two major islands Funen and Zealand and another 407 smaller islands, giving Denmark a long coastline of approximately 7300 km and a distinct physical boundary for the analysis. The climate is temperate maritime with winter and summer mean temperatures of 0.0 ◦ C and 15.6 ◦ C, respectively, and an average annual precipitation of 712 mm with regional differences of up to 300 mm (Danish Meteorological Institute, 2011; Statistics Denmark, 2010). Cultural development of the originally mostly forested Danish landscape has been at least 6000 years in the making (Fritzbøger, 1994). Forest cover now only accounts for approximately 13% (Johannsen et al., 2009), while agriculture occupies 62% of the total land area (Statistics Denmark, 2009), one of the highest percentages in Europe (Eurostats, 2014). Denmark also has a high population density of 128.4 pers/km2 (Statistics Denmark, 2010), with the strongest urban development in the metropolitan area of Copenhagen, on the east coast of Central Jutland and on Funen (Organisation for Economic Co-operation and Development, 2010). Raudsepp-Hearne et al. (2010) used Canadian municipality boundaries as their sampling units. As Danish municipalities are much larger than those in the Canadian study, and a uniform grid is better suited for spatial analyses because of consistency in area,

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we instead divided our study region into 10-km × 10-km grid cells, keeping only those with a land area > 10 km2 , resulting in 555 cells. 2.2. Sampling method Following the Canadian study, but modified as needed to fit the Danish situation, we collected proxies for 11 different provisioning, regulating, or cultural services, as defined in the MA (2005). We did not include the phosphorus retention and maple syrup services analyzed by Raudsepp-Hearne et al. (2010). 2.2.1. Provisioning services Livestock production (ANIMALS): The main types of livestock in Denmark are pigs and cattle, with approximately 1.63 million heads of cattle and 12 million pigs produced in 2009 (Statistics Denmark, 2010). The Canadian study only measured pig production but we included cattle as well. We converted the numbers of livestock produced per grid cell into Livestock Units (LU), since this is the operating unit for legislative purposes. One LU corresponds to 100 kg nitrogen produced per year in manure (Dalgaard et al., 2011a). The LUs have been computed for a 1-km × 1-km national grid based on the locations of farms with pigs or cattle for the years 1999–2008 (Kristensen & Kristensen, 2004), and we here used the mean per 10 km × 10 km grid cell, after square-root transformation to increase normality. Crop production (CROPS): In line with Odgaard, Bøcher, Dalgaard, and Svenning (2011) we used the national field-block scheme to locate crop production. Field blocks are administrative units of area used for processing farm applications for EU subsidies and each block covers 1–15 agricultural fields. The borders are relatively permanent linear structures in the landscape and each field block is given a unique ID number. All farmers applying for subsidies must register each year the crops they are growing, the size of the area covered by each crop and the ID number of the block in which the field is located. As this theme includes, for instance, hedgerows and small natural biotopes, the field block area overestimates agricultural cropland. Therefore, we could not use the field block areas directly, but linked the crop production data that all Danish farmers submit to The General Farm Register (Statistics Denmark, 2009) to the field-block data and aggregated the total area declared for each field block into 10-km × 10-km grid cells to estimate crop production as land under cultivation, similar to the method in the Canadian study. We recognize that measuring crop production area rather than yield may have some limitation since the largest production areas not necessarily produce the highest yields. Drinking Water (DRINK WAT): In Denmark drinking water is almost exclusively extracted from aquifers (Hansen, Thorling, Dalgaard, & Erlandsen, 2011) and the actual freshwater stock can be considered a provisioning ecosystem service. To create a proxy for the ecosystem service that provides drinking water, we used not only the aquifers but also their surrounding surface watershed. The areas providing drinking water are by the Danish Ministry of the Environment divided into two zones: “Areas of high interest” where the water is used for water supply on a regional scale, at present or in the future, and “Areas of low interest” where small-scale water supplies only have local or industrial use (Rygnestad, Jensen, Dalgaard, & Schou, 2002). We used the same categorization and characterized high interest areas as 1 and low interest areas as 0, mapping these to the 10-km × 10-km grid cells and calculated the area per grid cell. The Danish conditions are different from those used in the Canadian study where they were able to measure surface water as drinking water. The geospatial drinking water

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data were collected from the Danish Natural Environment Portal (www.miljoeportal.dk).

2.2.2. Cultural services Recreation and Ecotourism (TOURISM): We summed the amount of nature recreation facilities (scenic view points, footpaths, observation towers, bathing lakes, fishing areas, cycle paths, and associated amenities) per grid cell. Importantly, this data set does not include hotels and amusement parks or traditional types of tourism facilities, but only nature-related recreational infrastructure. These (n = 5725 in total) were registered by the Danish Nature Agency in the database ‘Nature Experiences’ where the geospatial data is accessible via an interactive map (www.udinaturen.dk). We summed the points, the path lengths, and the area per grid cell of the different facilities and standardized the sums by ([value]/[mean])/[standard deviation]. We used this as a proxy for ecotourism and recreational ecosystem services in accordance with the Canadian study. Bieling and Plieninger (2013) also found this method of quantifying cultural services useful for mapping. Nevertheless, as they pointed out this method of mapping the facilities gives a place of consumption, or enjoyment, but not service production site. Sense of place (CONS): This was measured as the conservation area per grid cell. A sense of place can be defined as an assembly of peoples’ beliefs, values and feelings tied to a certain locality (Williams & Stewart, 1998). We included areas protected under the Nature Conservation Act (Naturbeskyttelsesloven, 2008), many of which are selected through public debate and intended to protect cultural and natural heritage sites. Other types of conservation (e.g., the §3 and RAMSAR sites) were omitted because they were protected for reasons other than cultural or were not constant in their conservation status; nor did we include purely cultural heritage sites such as ruins or archeological sites. This service is not represented in the Canadian study and was quite distinct for the Danish case. We note that the CONS and TOURISM layers were very similar, but we think it was because these cultural values were, in general, closely linked in the landscape. For instance, a viewpoint, which by our definition is a recreational service, may easily be located within a conservation area and thus describe the same esthetic values or sense of place. The conservation areas totaled 2015.9 km2 (4.8% of the total land area) with a mean size of 0.438 km2 per area (www.miljoeportalen.dk). Nature Appreciation (NATAPPR): This service was measured as the number of sightings of species per grid cell submitted by people via the nature observation portal Fugle og Natur (www.fugleognatur.dk) for the year 2009. The website is used for reporting species sightings in collaboration with Aarhus Natural History Museum and has approximately 15,000 users, broadly distributed across the country, and about 570,000 entries (December 15, 2010). The data are an expression of nature appreciation since the people reporting the sightings were obviously nature appreciators and thus it is a combination of the species present, human population density, accessibility, and site attractiveness. The measurement differed from the Canadian study in that all species counted as an observation, not just the endangered ones, and thus it might capture a broader audience of nature appreciators. Summer cottages (COTTAGES): The sum of summer cottage area per grid cell was used as a measure of a cultural ecosystem service, since being close to nature and enjoying the esthetics of nature are considered major factors in the experience of owning or vacationing in a summer cottage (Larsen, 2010; Tress, 2007, Chap. 7). The summer cottage geospatial data were collected from the Danish Natural Environment Portal (www.miljoeportal.dk). We used the data for designated summer cottage zones to measure the area of

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summer cottages per grid cell; thus this layer did not reflect any changes in property values as the Canadian study did. This also meant we did not include summer cottages that were built outside these designated areas or were not originally designed as summer cottages. HUNTING: Hunting is a popular activity and a deeply rooted social phenomenon involving people from different social backgrounds (Fisker, 2009). Hunting in Denmark is mainly done for recreational purposes and thus classified as a cultural ecosystem service, whereas in other regions it may be a provisioning service where the main function is to provide meat for consumption (Knoche & Lupi, 2007; Raudsepp-Hearne et al., 2010). We focused our analysis on roe deer (Capreolus capreolus) hunting because it is a key game species in Denmark (Knoche & Lupi, 2007; Olesen et al., 2002). Hunting data were only available on municipality scale from the Danish Centre for Environment and Energy. A municipality covers on average an area of 433.9 km2 and was therefore not compatible with a scale of 100 km2 grid cells, nor was it comparable with the number of animal roadkill per square kilometer as was the case in the Canadian study. To translate the coarse-grained municipality data to more fine-grained hunting data we estimated roe deer density per grid cell from GPS registrations on roe deer killed in traffic (Andersen & Madsen, 2007). The number of roadkill in a grid cell can be taken as an indicator of roe deer density, but is likely to also be influenced by the human population density and road length per grid cell. We therefore built a regression model to provide a roe deer density estimate adjusted for the effects of the latter confounding effects. Our estimate of roe deer density was therefore the residuals of a regression model of roadkill numbers against human population density and road length. As the roadkill data exhibited overdispersion, we used a quasi-poisson distribution Generalized Linear Model (GLM) (Zuur, Ieno, & Smith, 2007) to adjust the number of roadkill per grid cell for human population density, land area in the cell, and summed length of roads (≥3 m wide). We centered the explanatory variables and included the centered quadratic, thereafter selecting the best model based on a goodness-of-fit in an F-test (Zuur et al., 2007). The residuals of the model were then used as an estimate of roe deer density per grid cell. There was spatial autocorrelation in these residuals (Z-score = 14.58). Such spatial autocorrelation may cause regression parameter estimators – and thus also the residuals – to be biased, although this is not always the case. We assessed this issue by refitting the regression model to a geographically dispersed subset of cells (n = 71), selected by using the center grid cell in each municipality, except for the small dense municipalities in the greater Copenhagen area, where three municipalities were randomly selected to avoid spatial autocorrelation. Therefore, this subset had no spatial autocorrelation in the residuals. The residuals from the regression model on the full data set (n = 555 cells) compared with those from the subset model had a Pearson’s correlation coefficient of 0.984 and therefore must also be unbiased; hence, spatial autocorrelation did not cause bias in the roe deer density estimate. Roe deer hunting per grid cell (HUNTING; the number of roe deer killed by hunters) was estimated by multiplying the municipality-level hunting pressure, as a percentage of the total roe deer hunting kills in Denmark, with the estimated roe deer density. The four equations of the analysis are set out below:

1. [Roadkill] ∼ [humanpop] + [humanpop]2 + [roadlength] √ + [roadlength]2 + [area] 2. [Roe deer density] ∼ observed([Roadkill]) − predicted([Roadkill]) 3. [Hunting pressure] ∼ ([Municipal hunting pressure] × 100)/[National hunting pressure] 4. [HUNTING] ∼ [Roe deer density] × [Hunting pressure]

2.2.3. Regulating services Wetland water purification (WETLAND): Wetlands facilitate wastewater treatment and nutrient absorption and also provide flood mitigation (Geber & Björklund, 2002; MA, 2005). We used freshwater wetland area per grid cell as a proxy for these services, with the total wetland area being 1099 km2 . The proxy does not differentiate between the varying quality nor use of wetland services, but is based on a land use area measurement. Data on wetland area were retrieved from TOP10 (Statistics Denmark, 2010). This service was not analyzed in the Canadian study. Forest carbon storage (CARBON): Forests provide significant amounts of above-ground carbon storage (Johannsen et al., 2009; MA, 2005) and was here measured as forested area per grid cell (cf. Dalgaard et al., 2011b). Forests additionally regulate local and regional climates, notably by reducing evapotranspiration and increasing surface albedo (MA, 2005). Denmark has a long history of forest protection (mainly to secure wood production), resulting in an increase in forest cover from approximately 2% in the early 1800s to about 13% today, corresponding to 5890 km2 (NordLarsen, Bastrup-Birk, Thomsen, Jørgensen, & Johannsen, 2010). Although forests of different types and ages store differing amounts of carbon (Johannsen et al., 2009; Nord-Larsen et al., 2010), we did not have data available to differentiate these. The forest cover data were retrieved from TOP10 (Statistics Denmark, 2010). This forest carbon storage proxy was a simpler land cover layer than that used in the Canadian study and was more comparable to their forest recreation service than their carbon sequestration measurements. Soil organic carbon storage (HUMUS): We used the amount of humus in the A-horizon of the soil as a measure for the organic matter storage below ground (Ponge, 2003; Powlson et al., 2011), calculated as the median percentage per grid cell from the national soil cover map by Greve et al. (2007). Thus, this layer is not only restricted to the agricultural areas as was the measurement used in the Canadian study. Humus also plays an important role in inhibiting soil erosion by increasing aggregate stability (Govers, Poesen, & Goossens, 2004) and retaining nutrients, storing toxic and heavy metal particles and providing high water storage capacity (Ponge, 2003). Soil organic content can also be influenced by, for instance, tilling and other soil management methods (Chatskikh, Hansen, Olesen, & Petersen, 2009); however, this proxy does not capture such variations. 2.2.4. Supporting services We did not include any supporting services in this analysis because they were already incorporated in the output of the other services and thus would be counted twice (Boyd & Banzhaf, 2007). 2.2.5. Data summary All ecosystem service values were individually summarized within each grid cell. CARBON, WETLAND, HUMUS, TOURISM, NATAPPR, CONS and COTTAGES were log-transformed and HUNTING was square-root-transformed, to give a more uniform distribution for Pearson’s correlation analysis and subsequently standardized for comparability across different measurement units and for Principal Component Analysis (PCA) by ([value]/[mean])/[standard deviation] (Laterra et al., 2012). We used Excel (Microsoft, 2007) for transformation, ArcGIS 10.1 (Esri, 2010) for spatial data processing, and R (R Development Core Team, 2008) and Spatial Analysis in Macroecology (SAM) 4.0 (Rangel, Diniz-Filho, & Bini, 2010) for statistical analyses. 2.3. Analyses 2.3.1. Spatial patterns Each ecosystem service measure was mapped in ArcGIS, 10.1 (Esri, 2010) and its spatial clustering quantified using Moran’s I in

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Table 1 Pearson’s correlations. Correlation analysis of the pairwise interactions between ecosystem services divided into positive, negative, and no correlations. High correlation 0.5–1.0 (dark gray), moderate correlation (medium gray) 0.3–0.5, weak correlation (light gray) 0.1–0.3, no correlation 0.0–0.1 (white).

SAM (Dutilleul, Clifford, Richardson, & Hemon, 1993; Rangel et al., 2010). 2.3.2. Interactions We used Pearson’s correlation (rp ) to assess the pairwise relations between ecosystem service measures. Significance levels for rp were corrected for spatial autocorrelation by adjusting the degrees of freedom using Dutilleul’s method (Dutilleul et al., 1993).

values that are more alike within the cluster than between clusters in the analysis. The K-means cluster analysis was run with 1000 iterations to maximally decrease the total error sum of squares (TESS) by moving the cells between clusters (Raudsepp-Hearne et al., 2010). The optimal numbers of clusters was qualitatively determined by examining the composition of services within the cluster (see Appendix D for discussion of cluster numbers). We used SAM 4.0 for PCA and K-means cluster analysis (Rangel et al., 2010) and ArcGIS 10.1 (Esri, 2010) for mapping of services.

2.4. Bundles 3. Results We used Principal Component Analysis (PCA) to quantify the main multivariate interrelationships between the ecosystem service variables as a first step in assessing if ecosystem services cooccur in spatial ecosystem bundles. Following the Kaiser–Guttman criterion (eigenvalue > 1) (Legendre & Legendre, 1998; Plieninger, Dijks, Oteros-Rozas, & Bieling, 2013) we concluded that the first four PCA axes were sufficient to characterize the non-random structure in the data. We then applied a K-means cluster analysis to identify the grid cell groupings according to their PCA axes 1–4 scores. A key advantage of using the PCA scores here is that the PCA axes represent uncorrelated components of ecosystem service data (Cf. Legendre & Legendre, 1998), so that no service directly or indirectly is counted more than once. Each cluster consists of grid cells with a composition of ecosystem service

3.1. Spatial patterns All ecosystem services exhibited spatial clustering (Fig. 2 and Appendix B), with significant positive spatial autocorrelation up to approximately 50 km distance. Of the provisioning services, agricultural provisioning services (ANIMALS and CROPS) were most strongly represented in the western and southern parts of Denmark, while DRINK WAT was most plentiful in the more densely populated areas of eastern Denmark (Fig. 3). The cultural services TOURISM, CONS and NATAPPR had a higher concentration in areas known for their recreational opportunities, i.e. northern Zealand, the lake district in central Jutland, the national parks in Thy (northwestern Jutland), Mols (eastern tip of

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Moran'sI

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0.600 0.400 0.200 0.000 -0.200 -0.400

a ANIMALS CROPS 10

30

50

70

90 110 130 150 170 190 210 230 250 270 290

DRINK_WAT

Dist(Km)

b

Moran's I

0.600 0.400

NATAPPR

0.200

TOURISM

0.000 -0.200

CONS 10

30

50

70

90 110 130 150 170 190 210 230 250 270 290

COTTAGES HUNTING

-0.400

Dist(Km)

c

0.600 Moran's I

0.400 0.200

CARBON WETLAND

0.000 10

30

50

70

90 110 130 150 170 190 210 230 250 270 290

-0.200 -0.400

HUMUS

Dist(Km)

Fig. 2. Moran’s I. Spatial autocorrelation measured for each of the 11 ecosystem services as a function of the distance. Positive values indicate a spatial clustering of all the variables at a minimum distance of 50 km. (a) Provisioning services, (b) cultural services, and (c) regulating services.

Fig. 3. Spatial distribution of ecosystem services. Each service is displayed in color coding corresponding to the service category and percentile occurrence summarized for the grid.

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Fig. 4. Principal component analysis biplot. The first axis represents a spatial trade-off between the three provisioning services on the one side and regulating and cultural ecosystem services (except HUNTING) on the other. This appears to give a separation of agriculturally dominated inland areas from coastal areas and other areas rich in regulating and non-hunting cultural services. The second axis shows a divide between high CARBON, NATAPPR and DRINK WAT values, and an ANIMALS, CROPS, WETLAND, COTTAGES and HUMUS composition, describing the gradient of coastal regulating services and the segregation of agriculture and forest-dominated landscapes. The third axis is highly descriptive of the distribution of HUNTING. The fourth axis illustrates conservation and wetlands in the west and other cultural services in the east.

Jutland) and the Wadden Sea (southwestern Jutland). NATAPPR had higher values than TOURISM in highly populated areas like eastern Jutland and Zealand, and, conversely, more TOURISM than NATAPPR in the less populated western Jutland. HUNTING exhibited a different spatial pattern as it was less prevalent in the urban areas and the national parks. COTTAGES and HUMUS were most important in coastal and low-lying areas, and HUMUS and WETLANDS had a higher concentration in western Denmark. CONS and CARBON showed the weakest coherence and were randomly dispersed at distances of approx. 50 km (Fig. 2b and c). 3.2. Interactions There were 41 significant pair-wise correlations between ecosystem services (20 positive, 21 negative) (Table 1 and Appendix C), with the strongest correlations between CROPS and ANIMALS and COTTAGES and HUMUS (both positive). Provisioning services had primarily negative correlations with the other services, except for the strong positive correlation between the two agricultural services and a weak positive correlation between CARBON and DRINK WAT. Regulating and cultural service classes had predominantly positive correlations. 3.3. Bundles

Table 3 Principal component loadings.

PCA was used for the analysis of bundles and the first four components accounted for 69.7% of the total variation (Table 2). The first principal component accounted for 30.8% of the variation Table 2 Principal component eigenvalues.

1 2 3 4

in ecosystem services and represented a spatial trade-off between the three provisioning services on the one side and regulating and cultural (except HUNTING) ecosystem services on the other. This appeared to separate provisioning services or agriculturally dominated landscapes from landscapes rich in regulating and non-hunting cultural services, primarily coastal areas. The second principal component accounted for an additional 18.5% of the variation in ecosystem services and primarily described the segregation of low-lying coastal areas, agricultural landscapes and forest-dominated landscapes (Fig. 4). Principal components 3 and 4 described gradients in HUNTING and major wetland areas (WETLAND and CONS), and accounted for 10.9% and 9.6% of the variation, respectively (Fig. 4 and Table 3). The K-means cluster analysis allowed us to identify the main ecosystem service groupings: a group dominated by agricultural provisioning services, a mixed-use type with medium values for services in all three classes, groups with high values for forest carbon storage and cultural services, and two coastal groups with high values for wetlands and cultural services. Specific combinations of ecosystem services characterized each bundle type (Table 4), and from the interpretation of these combinations, the six bundle types were named: (1) Agriculture, (2) Multifunctional,

Eigenvalues

Proportion

Acum.Prop.

Broken stick

3.385 2.03 1.197 1.051

0.308 0.185 0.109 0.096

0.308 0.492 0.601 0.697

0.275 0.184 0.138 0.108

ANIMALS CROPS DRINK WAT TOURISM NATAPPR CONS COTTAGES HUNT CARBON WETLAND HUMUS

PC1

PC2

PC3

PC4

0.614 0.57 0.484 −0.606 −0.549 −0.601 −0.809 0.008 −0.125 −0.463 −0.732

−0.466 −0.478 0.532 0.128 0.483 0.125 −0.283 0.031 0.707 −0.439 −0.512

−0.075 −0.081 −0.317 −0.16 −0.225 −0.183 0.134 0.928 0.304 −0.036 −0.05

0.001 −0.105 −0.125 −0.325 −0.024 0.579 −0.374 0.066 −0.016 0.586 −0.308

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Fig. 5. Ecosystem service bundle types. K-means cluster analysis revealed six distinct bundle types. The map shows the distribution of each bundle type and the diagrams show the average values of each service in the bundle type. Values are standardized and displayed on a scale from −2 to 2 where 0 is the individual service mean value, not the bundle type mean.

(3) Mixed Provisions, (4) Forest Recreation, (5) Coastal Recreation, and (6) Coastal Tourism (Fig. 5 and Table 4). The K-means clusters exhibited moderate to strong spatial coherence. The Agriculture bundle had the highest crop and livestock values and was also the most abundant type with 180 grid cells. Apart from the provisioning services, all other services had values below mean (Fig. 5 and Table 4). This bundle type was spatially clustered, occurring primarily in southern and northern Jutland and on Funen (Fig. 3). The Multifunctional bundle comprised 105 grid cells and this bundle type had mean values for most services, but below mean values for HUNTING, CARBON and ANIMAL (Fig. 5). Of all bundle types, this had the most evenly distributed provision of all services. The Multifunctional bundle types appeared near some of the major urban areas, notably four of the five biggest urban areas (Copenhagen/northern Zealand, Aarhus, Odense, Esbjerg). The Mixed Provisions bundle was represented by 106 grid cells and had values above mean for all the provisioning services as well as for CARBON and HUNTING, and below mean for the remaining ecosystem services (Fig. 5 and Table 4). It predominantly occurred in central and northern Jutland, southwest Zealand and on the islands of Lolland and Falster (Fig. 5 and Table 4). These bundles seemed to be associated with the Forest Recreation bundle types, as they often appeared in close proximity. The Forest Recreation bundle was especially characterized by high values of hunting and

the other forest-related services – forest carbon storage and nature appreciation (Fig. 5 and Table 4), where Mixed Provisions had higher values for agriculture and areas conserved for cultural purposes – sense of place. The two bundle types Coastal Recreation (n = 63) and Coastal Tourism (n = 37) primarily occurred in coastal areas, hence the name, and had quite similar service distributions with high values for HUMUS and WETLAND and the highest cultural ecosystem service values overall (Fig. 5 and Table 4). 4. Discussions Our study showed that key ecosystem services in the intensely cultivated region of Denmark formed spatial trade-offs and synergies at the landscape scale and could be grouped into ecosystem service bundles. 4.1. Spatial patterns The studied ecosystem services exhibited distinct spatial patterning and all were spatially clustered to a distance of 50 km (Fig. 2), some even more. The agricultural provisioning services were most prominent in western Denmark, while drinking water provisioning was most important in eastern parts (Fig. 3). Cultural services had the highest values at the coast and hunting was

Table 4 Bundle types. Bundle

N

ANIMALS

CROPS

DRINK WAT

TOURISM

NATAPPR

CONS

COTTAGES

HUNT

CARBON

WETLAND

HUMUS

Agriculture Mixed provisions Multi-functional Forest recreation Coastal tourism Coastal recreation

180 106 105 64 37 64

0.288 0.033 −0.123 −0.186 −0.487 −0.198

0.239 −0.061 −0.037 −0.212 −0.218 −0.175

0.325 0.398 −0.030 0.406 −0.522 −0.326

−0.143 −0.439 0.236 −0.621 1.320 0.610

−0.179 −0.204 0.151 −0.090 0.714 0.276

−0.174 −0.083 0.210 −0.248 0.474 0.258

−0.458 −0.412 −0.010 −0.309 2.210 1.036

−0.099 0.461 −0.528 1.347 −0.770 −0.529

−0.129 0.411 −0.218 0.862 −0.767 −0.384

−0.222 −0.135 0.087 −0.224 0.824 0.458

−0.426 −0.534 0.042 −0.583 2.465 1.191

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most important in central and northern Jutland and southwestern Zealand (Fig. 3). Among the regulating services, wetlands and soil organic carbon storage were also most concentrated at the coast, while forest carbon attained the highest values inland, with high clustering in certain areas such as central Jutland and northern Zealand (Fig. 3). The provisioning services exhibited the highest spatial clustering of the three categories (Fig. 2a). Notably, the agricultural services (ANIMALS and CROPS) clustered into specific areas, similar to the results of the Canadian study, with the highest values for agriculture in western Denmark, a less populated area. It may be argued that the CROPS function overestimates services from crop production in the poorer soils, primarily in western Denmark, while it is underestimated on the richer soils with higher yields in eastern Denmark (Khattak, 2008). Especially cash-crops have higher yield potentials here than on the sandy soils in the west (Odgaard & Rasmussen, 2000; Odgaard et al., 2011). We therefore recommend that future studies take this effect into account. However, the regional concentration of especially cattle production in western Denmark is well known (Hansen et al., 2012), and during the last century, grassland, maize silage and other types of highyielding forage crops linked to cattle production have also showed high yields on the irrigated sandy soils in western Jutland, allowing a synergy between crop and livestock production in these areas (see Section 4.2). Furthermore, the high use of livestock manures and ready access to fertilizers and other input factors have lessened the importance of natural soil fertility for yield and gross margin generation. Therefore, in addition to the above-mentioned synergy between livestock and high-yielding forage crop production, the average crop yields on irrigated sandy soils are today close to the yield on loamy soils, and irrigated sandy soils are even considered more suitable for high-value potato and carrot production than clay-loamy soils (Andersen, 2000, Chap. 7; Dansk Landbrugsrådgivning, 2013). Finally, the relatively low population densities and historically largely deforested landscapes in western Jutland cause less competition for area with other land uses and their associated services. In the Canadian study, Raudsepp-Hearne et al. (2010) also suggested that the predominance of agriculture in certain areas reflected their flat topography, which is similar to our findings of agriculture in the west. The regulating services were the least clustered service category, but showed distinct patterns in the landscapes. Both forest and wetland distributions were determined by historical factors and land management. Wetland areas were drained for agricultural purposes in some areas, completely removing wetlands from the landscapes (Kristensen, Reenberg, & Pena, 2009). Furthermore, the historical conversion of forest to agriculture had driven a spatial trade-off between the two land covers, decreasing the forest cover to about 2% (Odgaard & Rasmussen, 2000). This resulted in a loss of all the associated services and is a trend that is still occurring in many developing parts of the world (Geist & Lambin, 2002). Of the cultural services, the indicators of sense of place and tourism showed the least spatial clustering (Fig. 2b). The spatial pattern evident was likely a product of the localized policies and public debate. Landscapes that give a sense of place are valuable to people and have long been a rationale behind landscape management and planning (Williams & Stewart, 1998). In Denmark conservation areas are designated through lengthy processes of public and court hearings to determine if their cultural or environmental importance is higher than other, perhaps more profitable, land uses – a process going back to 1917 (The Danish Society for Nature Conservation, 2010). The conservation areas in the present study had an average size of 0.438 km2 and were found on 4599 different sites that showed little spatial congruence and had the lowest Moran’s I-value of all the studied ecosystem services (Fig. 2b and Appendix B). These patterns are typical for landscapes

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in a European context, where conservation efforts are in intense competition with urban or agricultural land uses (Vos & Meekes, 1999) and thus become focused on small conservation areas, in contrast to, e.g., North America (Tscharntke, Klein, Kruess, SteffanDewenter, & Thies, 2005). The dispersed small conservation sites may be important for adding to the landscape complexity in the agricultural landscapes that dominate much of Denmark and may increase biodiversity and ecosystem resilience (Ellis et al., 2010; Tscharntke et al., 2005). One factor that has been shown to have an influence on conservation and the management of conserved areas is the population pressure and land use/-price properties (Ando, Camm, Polasky, & Solow, 1998). This could be an important influence since the Danish conservation offers an economic compensation for the landowner and is thus more cost-efficient at lower land prices (The Nature Conservation Act, 2008). RaudseppHearne et al. (2010) found tourism to be randomly distributed in the Canadian study. In contrast, tourism facilities in Denmark were concentrated in areas of high population density or recreational appeal, such as the forested areas in the lake district in central Jutland, in or near the national parks and especially at the coasts with other cultural services. 4.2. Interactions The many positive and negative associations between the different services highlight their complex interactions. Overall, it was especially notable that the cultural services were positively correlated with each other as well as with the regulating services, suggesting synergistic or at least non-antagonistic relationships, while the agricultural provisioning services (ANIMALS and CROPS) generally exhibited negative relationships to the cultural and regulating services. These general patterns probably reflect that agricultural land use will limit the availability of land for most other services (Foley et al., 2005; Vos & Meekes, 1999), while most cultural and regulating services and associated land uses are less mutually exclusive, if at all, and thus more easily combined (DeFries, Foley, & Asner, 2004). The latter fact was consistent with the findings in the Canadian study, although they found that of the two, regulating services were more likely to form synergistic relationships, whereas in our study this was cultural services. One explanation could be that cultural services like nature appreciation and tourism did not occupy as much land as regulating and provisioning services, where, for example, crop production and wetland water regulations were dependent on an actual site for functionality (MA, 2003; Rounsevell, Annetts, Audsley, Mayr, & Reginster, 2003). Hunting was the service least correlated with other services; thus it did not seem to be in conflict with any other services we measured and could coexist with other land uses in the area. The weak positive correlation to forest carbon storage was logical since forests and small wooded biotopes are important refugees for roe deer (Radeloff, Pidgeon, & Hostert, 1999). The indicator for sense of place was positively correlated with those for nature appreciation and tourism, exemplifying a win–win situation for landscape development with economic incentives for protecting localities important for cultural and natural heritage (Schroth & McNeely, 2011). The indicator for sense of place appeared to be moderately traded off with crop production, probably reflecting that modern large-scale crop production strongly competes for land with natural and traditional semi-natural land uses, as previously mentioned. The synergistic pattern for sense of place and nature appreciation was also visible for carbon storage. It was the most abundant regulating service in this study, and climate change regulation is globally one of the few increasing regulating services because of international carbon trading markets (Carpenter et al., 2009; Nabuurs et al.,

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2007; Nelson et al., 2009). We note that these correlations were largely coincidental here. High carbon storage occurs in forested areas in Denmark (Dalgaard et al., 2011b; Johannsen et al., 2009), as was also found in the present study. Forests in Denmark have been preserved or planted with trees for timber production as the main aim (Graae, 2000), with all the services considered here as coincidental benefits. Nevertheless, there is a long history of multiple land use combinations with carbon storage around the world (Vincent & Binkley, 1993), e.g., hunting, ecotourism, recreation and biodiversity protection (Bai et al., 2011; Foley et al., 2005), as also displayed in our results for Denmark, and for Canada (Raudsepp-Hearne et al., 2010). Such correlations provide incentives for afforestation and reforestation projects or increase the viability of projects as they can increase livelihood diversity through income opportunities and economic growth in the area, of course depending on their more specific implementation (Davis, 2003; Grieg-Gran, Porras, & Wunder, 2005; Reardon, Berdegué, Barrett, & Stamoulis, 2006, Chap. 6; Schroth & McNeely, 2011). Most likely reflecting competition for land use, the agricultural provisioning services (ANIMALS and CROPS) exhibited negative correlations with forest cover and associated carbon storage. This is currently a highly regulated competition, as Denmark has a twocentury long policy of forest protection under which most forested areas are permanently set aside for forest, restricting the expansion of agriculture (Johannsen et al., 2009; Odgaard & Rasmussen, 2000). Increased segregation will probably continue globally in the future, if development sees traditional agriculture give way to industrialized farming methods (Carpenter et al., 2009; Ellis et al., 2010; MA, 2005), and management of landscapes would have to mitigate such factors to increase multifunctionality (Vos & Meekes, 1999). The high positive correlation between livestock and crop production represented a well-known synergy, applicable to both industrialized production in the Canadian study (Raudsepp-Hearne et al., 2010) and to small-scale and subsistence farming (Devendra & Thomas, 2002). Livestock farmers need to have enough land for the disposal of their manure to comply with the EU Nitrates Directive, i.e., 1 ha per 1.7 livestock units (Kronvang et al., 2008). Moreover, the combination of crop and livestock production is an advantage for the on-farm production of roughage and other feedstuffs (Odgaard et al., 2011), and high-yielding roughage crops like maize and silage grass increase the opportunity for livestock manuring. In other cases, however, crop production has been found to have a negative relation to animal production (Laterra et al., 2012). This is likely an expression of spatial and geographic differences in landuse practices, for example the conversion of pasture land to crop production (Laterra et al., 2012). The only regulating service that was uncorrelated with the agricultural provisioning services was the wetland water purification indicator. This contrasted with the general trend for a negative relation between the agricultural provisioning services and the cultural and the regulating services. The regulating effects of wetlands are important in mitigating agricultural runoff, especially nitrates and phosphates (Muscutt, Harris, Bailey, & Davies, 1993). With an increase in intensive agriculture, it is important to find ways to increase wetlands to protect against freshwater and marine eutrophication (Larsen & Harvey, 2010), e.g., via extended riparian non-agricultural buffer zones. Drinking water provisioning did not correlate with any of the agricultural provisioning services, which is different from the Canadian study. This may reflect the conversed geographic gradients between drinking water and the agricultural services, which is based on soil types and perhaps also partially driven by patterns in human population density to the west. Furthermore, drinking water in Denmark is extracted directly from aquifers (Hansen et al., 2011) and not from surface water as in the Canadian study area (Raudsepp-Hearne et al., 2010; Zebarth et al., 1998). We note

that our measurements of aquifer zonation did not reflect finely defined trade-offs between agriculture and drinking water provisioning, e.g., specific areas of nitrate vulnerability or low soil phosphorus retention, key factors that correlate negatively with agricultural services in the Canadian study as well as in other studies (Almasri & Kaluarachchi, 2004; Bennett et al., 2009; Fenger & Frich, 2002). Some correlations probably reflected coincidental rather than causal correlations. Soil organic carbon storage appeared in two of the highest correlations, but these were possibly due to geological and other geographic differences that caused highly humic soils to be located in the west and aquifers in the east. Most summer cottages also tend to be situated by the coast due to the high amenity values here (Larsen, 2010; Tress, 2007) and often in close relation to low-lying areas, driving a positive correlation with the HUMUS layer.

4.3. Bundles The ecosystem services not only exhibited pairwise interactions, but also consistent multivariate groups that allowed ecosystem bundle types to be defined, as in the Canadian study. Similar to that study, the ecosystem bundles identified in the present study also exhibited distinct geographic patterns. Bundle types have also earlier been shown to group into agriculture, forest, and various mixed uses in Britain (Dick et al., 2010) and in Canada. Hence, this may be a general pattern in the regional organization of ecosystem services. The Agriculture bundle type was the most abundant in the current study and showed the highest values of the agricultural services. There were distinct signs of spatial trade-offs between provision services and all other services, which was consistent with Denmark having the highest agricultural land use pressure in Europe. The Canadian study does not have quite as clear a provisioning service trade-off in their bundle type results, which seem to be separated into two main agricultural landscape types instead of just the one. The coast was a novel landscape feature in our study compared to the Canadian study, reflecting the importance of the coastline as a landscape feature in Denmark. Coastal Tourism and Recreation had more prominent regulating services than any other services and these bundle types were also accompanied by the highest cultural values of all. The most evident segregations between the services were the coastal–inland or provisioning–cultural services partitioning revealed by the first axis in the PCA. The two Coastal bundle types were very important cultural service providers in Denmark, and a good example of a synergy between cultural and regulating services. They were scattered along coastal urban areas and important vacation and summer cottage zones (Tress, 2007), and these areas have been managed for cultural and recreational services. Coastal areas have been shown to be of great cultural value in both Britain and Norway for tourists and local inhabitants (Fyhri, Jacobsen, & Tømmervik, 2009; Norton, Inwood, Crowe, & Baker, 2012), and tourism and recreation is an important part of the economy for coastal areas in Denmark. There is a tendency for peri-urban landscapes to be important areas for cultural service sites – the Multifunctional bundle types situated around the larger cities. The Multifunctional bundle type had the most diverse collection of ecosystem services of all the groups, with only forest carbon storage and hunting showing signs of spatial trade-offs. Such service diversity has been linked to a higher prevalence of regulating services and higher biodiversity (Nelson et al., 2009; Raudsepp-Hearne et al., 2010; Tscharntke et al., 2005). If that is the case, the multifunctionality of peri-urban recreational areas could be important areas for biodiversity conservation in cultural landscapes (Alberti, 2005; Davies, Edmondson,

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Heinemeyer, Leake, & Gaston, 2011; La Greca, La Rosa, Martinico, & Privitera, 2011). The Forest Recreation bundle type had the highest forest cover and had more hunting and drinking water than the Mixed Provisions bundle type. These two bundle types often seemed in close proximity on the map and they were similar in their service composition, where higher levels of forest carbon storage, hunting, nature appreciation and drinking water corresponded to lower levels of agricultural services, tourism and conservation indicators for sense of place; a fact also to some extent illustrated by the second axis in the PCA. These bundle types were quite similar to the forested, cultural-regulating bundle types (called “Villages” and “Country Homes”) identified in the Canadian study. Our findings had other implications for landscape planning. For example, in the correlation analysis there was a moderate, positive correlation between tourism and nature appreciation. This pattern was not evident in the Mixed Provisions and Forest Recreation bundles where mean values of nature appreciation were not reflected in the average availability of tourism facilities and there might be potential for more cultural services in these areas. The use of simplified land use/land cover data when mapping ecosystem services may only provide a relatively limited amount of information (Borgström et al., 2006). Notably, the assumption that a given land cover type has the same value for all sites can be a problem for ecosystem service assessments (Eigenbrod et al., 2010; Plummer, 2009). While we, in the present study, included as many direct measurements of ecosystem service availability and utility as possible, certain services had to be represented by land cover variables, namely the CARBON, WETLANDS, COTTAGES and DRINK WAT variables. Nevertheless, the quality of the data that were used in the present study was equivalent or higher than the data used by Raudsepp-Hearne et al. (2010). The coarse 10-km × 10-km grid of the present study, could also be viewed as a limitation, as information on the fine-scale processes that some ecosystem services rely on cannot be represented (Laterra et al., 2012). As is the case with most cultural landscapes, Danish landscapes are heterogeneous and consist of a mosaic of smaller biotopes embedded within other land use types (Caspersen & Fritzbøger, 2002; Poulsen, Larsen, & Dalgaard, 2002), consistent with other European landscape types (Vos & Meekes, 1999). Thus, it will be valuable to investigate ecosystem service distributions and interactions across Denmark and its geographically varying landscapes at finer spatial resolutions.

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5. Conclusions The present study quantified the geographic distribution of ecosystem services and their spatial inter-relations across an intensively cultivated region. Importantly, there was a strong tendency for cultural services to be potentially vulnerable to trade-offs with agricultural provisioning services and regulating and cultural services to be able to form synergies. The studied ecosystem services exhibited clear non-random geographic patterns with several regional gradients. One gradient broadly reflected the increasing demands for cultural and recreational services at the coasts and the denser populated parts of Denmark, and the socio-geographic drivers of agriculture as well as areas with low-lying wetlandhumus regulating services to the south and west of the country. The ecosystem services formed six multivariate bundle types with distinct composition and non-random spatial distributions, but it must be noted that numbers, types and spatial distributions of such bundles are sensitive to the ecosystem services selected for the study and to the input data available to define these services selected. However, in summary, the general pattern of agricultural, forest and mixed use bundle types was consistent with similar ecosystem service bundles and associated socio-ecological land-use dynamics described for other temperate regions, with additional distinct coastal bundle types in the Danish case, reflecting the regions’ long, both culturally and ecologically important coastline. Acknowledgements This work has been supported by the www.dNmark.org Strategic Research Alliance (DNMARK: Danish Nitrogen Mitigation Assessment: Research and Know-how for a sustainable, lowNitrogen food production, 2013–2017) funded by The Danish Council for Strategic Research (Ref. 12-132421) and the Aarhus University Research Foundation IDEAS program. We also thank the people who helped acquire data for the analyses: Inge T. Kristensen and Dr. Mogens Greve, Institute of Agrobiology, and Poul Erik Andersen at the Danish Centre for Environment and Energy, Aarhus University; and Tobias Fjeldstrup Skjoldager at the Danish Nature Agency. Finally, we appreciate the anonymous reviewers for comments to significantly improve this manuscript, and the results of our analyses. Appendix A. HUNTING specifications

Hunting data

Road kills/municipality

700 600 500 400

R² = 0.7038

300

Average 08/09

200

Linear (Average 08/09)

100 0 0

1000

2000

3000

4000

5000

Hunng kills/municipality

The record of hunting kills was only available on a municipality scale that on average covered an area of 433.9 km2 , more than four times larger than our grid cells. To increase the accuracy we included a GPS-point registration map of roe deer roadkills. We performed a linear regression with roadkill per municipality as the response variable and hunting kills per municipality as the predictor, which showed they were highly correlated with R2 = 0.7038.

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We corrected for an estimate of traffic load by modeling the number of people residing in the area, the area of the grid cell, and the length of roads in each cell (>3 m wide) against the number of animals killed in traffic. Population density and road load data were standardized by centering on the mean, and we conducted a quasi-poisson distributed generalized linear model (GLM) regression with roadkill as the response variable in R (R Development Core Team, 2008). The GLM corrected for overdispersion in the count data. To select the right combination of predictors when dealing with quasi likelihoods, we performed a goodness-of-fit F-test (Zuur et al., 2007). The residuals of the roadkill data showed considerable spatial autocorrelation. To test that the model was not influenced by this problem, we refitted the fullsize model of Roadkill (n = 555) on a

subset of cells (n = 71) that were dispersed throughout the country so that spatial autocorrelation should be minimized. There was no significant spatial autocorrelation in the residuals of this model. The correlation between the predicted values from the model using the full data set and the subset model was 0.9843, showing that spatial autocorrelation did not cause any important bias in the parameter estimates of the full model on the complete data set. Thus, we feel confident that the residuals of this model provide a reasonable estimate of fine-grained variation in roe deer density. The rescaled residuals were therefore used to represent roe deer density per grid cell in the subsequent analyses. We finally calculated hunting pressure from the original hunting data in percent per grid cell and multiplied it with the roadkill layer, resulting in an estimate of the cultural service of hunting. Appendix B. Moran’s I

Significance of Moran’s I across the study site. P < 0.001 (dark gray); P < 0.05 (light gray); P = ns (white) (Legendre & Legendre, 1998; Rangel et al., 2010)

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Appendix C. Correlation analysis

Pearson’s correlation coefficients (top right half) and the spatially adjusted significance levels (lower left half) (Rangel et al., 2010). High correlation (dark gray) 0.5–1.0; medium correlation (medium gray) 0.3–0.5; weak correlation (light gray) 0.1–0.3; no correlation (white) 0–0.1 (Legendre & Legendre, 1998).

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Appendix D.

K-means results for five and seven clusters. Diagrams describe the value of each service within the cluster, and colors refer to the six bundle types in Fig. 5. Considering five or six clusters did not change overall patterns in the landscape, except for aggregation of clusters: with five clusters Forest Recreation and Mixed Provision were lumped. Considering K-means results for seven clusters revealed an additional gradient-cluster between Agriculture and Multifunctional. We qualitatively chose the six cluster representation (shown in Fig. 5) because these bundle types were more detailed than the five clusters, and more concise than the seven clusters. The five cluster representation was very colored by the fact that the PCA revealed a strong coastal-inland segregation of services. This meant that a lot of the variation in the data lay in the small and marginalized coastal bundle type composition and information on the inland composition was rather lost. The seven cluster representation on the other hand was describing differences in the landscape that we found to be too detailed for this type of coarse scale analysis. Dick et al. (2010) reported difficulties

in pinpointing mixed use bundle types using a simplified dataset, which was exactly the differentiation that appeared when adding additional seventh cluster. Total error sum of squares (TESS) for the K-means cluster analysis described the internal variations in the dataset. The TESS analysis did not reveal any distinct breaks as shown in the chart below. It can be argued that there is a slight reduction of TESS after the fifth bundle type but it was so minor that we felt it adequately possible to include the information from the sixth bundle type. References Alberti, M. (2005). The effects of urban patterns on ecosystem function. International Regional Science Review, 28(2), 168–192. Almasri, M. N., & Kaluarachchi, J. J. (2004). Assessment and management of long-term nitrate pollution of ground water in agriculture-dominated watersheds. Journal of Hydrology, 295, 225–245. http://dx.doi.org/10.1016/j.jhydrol.2004.03.013

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