Ecosystem services are coproduced by humans and nature as a result of interactions between ecological functions and societal management and demand (Schröter et al. 2012, Reyers et al. 2013). However, understanding how this social-ecological coproduction of multiple services occurs in specific landscapes is poorly understood. Social-ecological interactions often produce distinct patterns of ecosystem services across landscapes, in the form of coherent sets of ecosystem service bundles (Bennett et al. 2009, Raudsepp-Hearne et al. 2010, Hanspach et al. 2014, Queiroz et al. 2015). Management or policy measures that aim to enhance a single, particular service will miss this complexity and can lead to perverse effects caused by hidden trade-offs among services such as those between crop yield and water quality (Tilman et al. 2002, Zhang et al. 2007), and timber extraction and carbon storage in forests (Putz and Romero 2001, Nelson et al. 2008). Trade-offs are often found between provisioning servicers and regulating or cultural services. Explicitly focusing on ecosystem service interactions and, in particular, bundles of ecosystem services is useful for the management of complex landscapes because it avoids those pitfalls. Furthermore, it helps identifying interventions that can have simultaneously desired effects on multiple ecosystem services (Queiroz et al. 2015). Thus, a crucial challenge is to identify and understand the key drivers, and their interactions, that produce these coherent sets of ecosystem service bundles across landscapes.
Because governments, NGOs, and companies are increasingly interested in incorporating ecosystem services into their decision making, there is a need for ways of predicting and quantifying the production, management, and use of ecosystem services. However, the current available tools are often complex and do not match data availability or existing decision or policy scales (Daily et al. 2009, de Groot et al. 2010). We test how easily accessible data can be used to understand the distribution of multiple ecosystem services across a spatial context that is relevant for decision making, e.g., multiple municipalities in a human-dominated landscape. Previous research has shown that spatial patterns of provisioning, regulating, and cultural ecosystem services produced in a landscape are driven by a mix of social factors, such as distance from a city, and ecological factors, such as topography and land use (Raudsepp-Hearne et al. 2010). We compare the ability of different models of human-environment interactions to explain patterns of ecosystem services observed across the Norrström drainage basin.
Different academic disciplines have emphasized distinct variables in their analysis of human-environment interactions (Briassoulis 2000, Lambin et al. 2006, Hersperger et al. 2010). We use established theories of human environmental interaction from human ecology, political science and economics, ecology, and geography (Table 1). We adapt four dominant alternative models of human-environment interactions to assess the importance of different potential drivers of ecosystem services as well as understand the strengths and limitations of these theories in the context of ecosystem services. We define drivers as the fast and slow changing variables, including exogenous controls, like slope (Carpenter et al. 2009). Ecologists have proposed that land use or land cover integrates social-ecological interactions and is therefore a useful proxy for assessing how humans interact with landscapes. This proposition is less of a model than an assumption that has been underlying many ecosystem services assessments, in which land use or land cover is accepted as the main determinant for the potential supply of ecosystem services (Costanza et al. 1997, Burkhard et al. 2009, Nelson et al. 2009). Political science and economics have proposed ecological modernization models that suggest that environmental quality improves as societies become wealthier and more developed (Kuznets 1955, Grossman and Krueger 1995, Mol 2003). From human ecology, the ecological footprint approach has highlighted the importance of social driver, e.g., population density, affluence, and technology, in driving negative environmental impact and consequently affecting ecosystem service distribution patterns (Stern et al. 1992, Chertow 2000, Harrison and Pearce 2000, York et al. 2003). Finally, geography has shown how distance and the cost of transport strongly shapes land use (von Thünen 1826, Alonso 1964, Griffith 1999), suggesting that variables such as distance from a city and topography, have a strong impact on activities performed across the landscape.
Our study region is the Norrström drainage basin, which is situated in southcentral Sweden. It covers 22,650 km² and includes two of Sweden’s largest lakes, Lake Hjälmaren and Lake Mälaren as well as Stockholm, Sweden’s largest city. The Stockholm metropolitan area, which is inhabited by approximately 1.5 million people, occupies the eastern part of the region and contains most of the population and economic activity of the region, and is the economic and political center of Sweden. Lake Mälaren supplies the drinking water for the Stockholm metropolitan area. The Stockholm region has a maritime climate; it is urbanized, and has high population growth. Around Lake Mälaren, the region is dominated by agricultural land, which is primarily used for the cultivation of cereals and rapeseed, and pasturage. The westernmost parts of the region have a more continental climate. Commercial forests characterize the land cover and land use in this part of the basin. This area has been experiencing a decline in population over the last 30 years as people have moved to more urban areas.
A variety of ecosystem services are produced, managed, and used in the Norrström drainage basin. Not only do the lakes in this region provide drinking water for the surrounding population, but they also serve as an area for recreation as well as a reservoir, retaining nutrients before they are released into the Baltic Sea. Pollinator habitats are abundant around agricultural areas, supporting insect pollinated crops and pasture areas. In Sweden, there is demand for summer cottages, outdoor recreation, the reporting of sighted biodiversity, and moose hunting, all these supported by both open habitats and forests. The forest areas across the basin also provide a range of commercial products, such as timber, paper fiber, wild berries, and mushrooms among others. In a previous study we quantified and mapped 16 ecosystem services (Queiroz et al. 2015), which we use as the ecosystem service information in this study (see Appendix 1). The services included were those that had been expressed as of interest by policy documents, regional planners, and other stakeholders in the region. Table 2 provides a complete list of those ecosystem services.
Queiroz et al. (2015) showed that the Norrström drainage basin is characterized by five ecosystem service bundles, which are spatially clustered across the basin. Each bundle type is characterized by a distinctive arrangement of the ecosystem services, represented by flower diagrams in Figure 1. Two of these bundles, Mosaic cropland horse and Mosaic cropland livestock, are characterized by high values of regulating services and provisioning services such as crop production, but differ in their levels of livestock production. The Forest and towns and Remote forests bundles are defined by the high provision of forest products, but differ with respect to certain cultural services such as moose hunting. The final bundle, Urban, is spatially constrained to the municipalities in metropolitan Stockholm and had the highest values of cultural services such as cross-country skiing and biodiversity appreciation.
Four candidate models, Land Use, Ecological Modernization, Ecological Footprint, and Location Theory, were created by carefully considering different key theories of human-environmental interactions and distilling the different driver variables that each theory emphasizes. The theories included in this paper are studied and cited theories on human-environment interactions prominent in different academic disciplines (Dietz et al. 2007). Each theory postulates what are key driving forces behind these interactions. We interpreted these driving forces into measurable driver variables to construct each model. We tested how these four candidate models predicted patterns of ecosystem service production in the study region. We also created a reference model that combines all the drivers used in the different candidate models.
We used information from public databases to quantify the different social, ecological, and geographic variables that the alternative key theories suggested were drivers of human nature interactions (see Appendix 2). Variables used in the candidate models (Table 1), to capture these drivers, were also based on expert knowledge of the region, data availability, and spatial heterogeneity across the landscape. Below we describe the candidate models, and explain the driver variables included, in more detail.
The land-use driver category represents different suites of ecological factors that are managed for. The use of land use stems from human ecology and emphasizes that it is the biophysical landscape that constrains human activity (York et al. 2003). Land use has been widely used as a proxy of ecosystem service supply in ecosystem service assessments (Costanza et al. 1997, Burkhard et al. 2009, Nelson et al. 2009).
Ecological modernization predicts that increasing socioeconomic development results in ecological degradation until a point when environmental conditions improve as societies become increasingly mature and affluent and begin to demand environmental quality (Kuznets 1955, Grossman and Krueger 1995). The ecological modernization perspective is sometimes represented by the environmental Kuznets curve and postulates that economic development will provide solutions to environmental problems (York et al. 2003).
The ecological footprint concept emphasizes that the environment provides resources, living space, and acts as a sink for waste. As population density and economic intensity distances populations from the supply of resources and their waste, there is inefficient use, degradation, and exploitation of these regions (Jorgenson 2003, York et al. 2003). The ecological footprint concept is an articulation of the IPAT formulation (Stern et al. 1992, Chertow 2000, Harrison and Pearce 2000) that states that environmental impact is equal to the multiplication of population density, affluence, and technology.
Location theory stems from geography and economics and supposes that the distance from an urban center will determine the land-use type and activities available for that area. The values of land, along with the heterogeneity of the landscape are the constraints in the spatial distribution of activities in the landscape (von Thünen 1826, Alonso 1964, Griffith 1999).
We calculated values for each of these drivers for the 62 municipalities in this landscape. Some of the drivers are correlated with one another, for example distance from Stockholm and income, but these correlated drivers were included in the analysis because of their different theoretical contributions, for example the Location Theory and Ecological Modernization models. Table 1 lists the drivers used across the four models tested: Land Use, Ecological Modernization, Ecological Footprint, and Location Theory.
We used QGIS 2.0.1 to map the spatial distribution of each individual driver by municipality and R to conduct all statistical analyses and produce all the figures (R Core Team 2013). The R packages ggplot2 (Wickham 2009), sp, and maptools (Bivand et al. 2013) were used to create the figures.
We identified correlations among drivers by comparing all the drivers to each other using the Pearson’s correlation coefficient. We compared how the alternative candidate models predicted individual ecosystem services by fitting ordinary least-squares multiple linear regression models of each model to each service, and then compared these modes using adjusted R² and the Akaike information criterion (AIC) to compare models for predictive power and parsimony. When comparing the fit of several models, AIC provides a criterion (penalized log likelihood) with which to find the model that best explains the data with a minimum of free parameters (Akaike 1974, Burnham and Anderson 2004). We also used a correction for small sample sizes (AICc).
We conducted a principal component analysis of instrumental variables (PCAIV), using the R package ade4 (Dray and Dufour 2007), to determine how the assessed drivers and assessed ecosystem services were related to one another. PCAIV is a method, similar to redundancy analysis and canonical correspondence analysis, that is used in ecology to study the relationships between the composition of species communities and their environment, by matching a sites-by-environmental-variables table and a sites-by-species table. Here we adapted it to analyze the relationship between sites-by-drivers and sites-by-ecosystem services.
To identify the relative importance of each individual driver variable (independent of correlation among variables) in predicting individual ecosystem services, we used hierarchical partitioning. This is a statistical method that analyzes all possible models in a multiple regression to identify the relative contribution of each variable to the total variance, both independently and in conjunction with the other variables, to estimate the impact of each variable (Chevan and Sutherland 1991, MacNally 2002). Specifically, we used the hierarchical partitioning to test the independent effect of all drivers, except slope on the prediction of each ecosystem service. We excluded slope, because hierarchical partitioning is only able to simultaneously test 9 variables, and slope was the only variable that was not significant in any model of ecosystem services. We used the R package “hier.part” (MacNally and Walsh 2004). We assumed Gaussian errors and calculated goodness of fit using R², and the statistical significance of the independent effect of each variable was estimated using a randomization approach (n = 1000) to calculate Z scores (MacNally 2002).
We used random forests, using the R package randomForest (Liaw and Wiener 2002), to assess and compare how well the alternative candidate models predicted both individual services and bundles of ecosystem services across the study landscape. This approach provides robust results for highly correlated predictor variables and small sample sizes (Prasad et al. 2006, Cutler et al. 2007). The random forest routine was used to predict the most likely ecosystem service bundle in each of the municipalities, but also provided the votes the random forest gives for each bundle. This allows the uncertainty of different predictions to be compared. The random forest analysis constructs a multitude of decision trees and produces a mean prediction of the individual trees. By comparing across individual trees the random forest allows the relative importance of different predictor variables to be assessed. We did this by calculating 100,000 trees for each model, and then calculating the accuracy of the bundle prediction as the percentage of correct predictions; because some ecosystem service bundles are more similar to each other we also assessed how well the prediction of bundles actually predicted ecosystem services in each municipality.
We also used the results of the random forest to predict ecosystem services in each bundle by two methods. First, the average level of the set of ecosystem services predicted determines the bundle for each municipality. The second method is the voting method. Each iteration of the random forest selects a bundle and that is counted as one vote for that bundle and the weighted average of the votes for different bundles of ecosystem services is the result. These two methods only produce different results if there is significant vote for different bundles of ecosystem services.
Comparing the results of the linear models to the results of the randomForest is not straightforward, however, we can compare the predictions of ecosystem services from each approach by calculating R². This allows us to compare model predictions to observations, but this approach does not adjust for the increased complexity of the linear models, and the risk that they overfit the data. More robustly comparing model predicts require larger numbers of municipalities or other ecosystem service delivery units, and represents an area of future research.
To test for unaccounted effects of spatial autocorrelation on our analysis we calculated Moran’s I on the residuals of the model’s predictions of ecosystem services, accounting for shared boundaries between municipalities. We also plotted model residuals for each municipality by ecosystem service and model. This approach allows us to assess whether models have adequately accounted for spatial autocorrelation among the ecosystem services, or if there is still substantial unexplained autocorrelation, and we use it as diagnostic of how well models explain ecosystem service pattern rather than to create spatial regression models (Dormann et al. 2007).
Each of the 10 drivers analyzed showed distinct patterns of spatial distribution across the basin (Fig. 2). All social drivers, wealth, income, population density, and education, exhibited highest values in the Stockholm metropolitan area. In contrast, land-use drivers exhibited more complex patterns. For example, forest area had relatively high values across the basin with the exception of the urban municipalities around Stockholm, while the percentage of built-up areas was highest in the Stockholm municipalities. The proportion of arable and grazing land was relatively higher on the municipalities around lakes Mälaren and Hjälmaren and some coastal municipalities north of Stockholm. Finally, the geographical drivers highlight the contrast between the northwest part of the basin with higher slopes, located further away from the city of Stockholm, and the relatively flat areas around lakes Mälaren and Hjälmaren, that were also closer to Stockholm.
We found strong negative and positive correlations between several of the drivers. Notably, the strongest correlations were not found between drivers of the same category (social, ecological, or geographical). Instead, two distinct clusters of positive correlations emerged; the first capturing the positive relationship between forest area and distance from Stockholm, while the second showed positive correlations between wealth, income, population density, education and built-up area (see Fig. A3.1). These two groupings are negatively correlated with each other; distance from Stockholm and forest area are both negatively correlated with wealth, income, population density, education and built-up area. Arable and grazing land were strongly positively correlated with each other and exhibited a strong negative relationship with slope and a weaker negative correlation with forest area. Grazing areas also showed a strong negative correlation with distance from Stockholm, whereas arable land showed no strong correlation with this variable. This suggests a trade-off between the forested areas and urban type areas. These types of relationships are explored more closely in the PCAIV analysis that follows.
The relationship between drivers and individual ecosystem services is represented by the results obtained with the PCAIV (Fig. 3). Figure 3a represents the distribution of ecosystem services with relation to the drivers. Figures 3b and 3c show the distribution of municipalities based on the relationship of their ecosystem services and drivers. The lighter colors in Figure 3b show the municipalities where the driver variables better explain the ecosystem services. The different colors in Figure 3c represent the different bundles of ecosystem services to which the municipalities belong. This analysis identified two dominant axes of the relationship between drivers and ecosystem services that together explain about 75% of the variation across municipalities. The first axis represents urbanism or connection to Stockholm; it is defined by a combination of population density, wealth, income, education, and built-up land. Thus, it separated urban municipalities located near Stockholm, and where population density, education, wealth, and income are relatively higher, from more rural municipalities. The second axis represented a gradient from agricultural land to forest dominated land. This axis was defined primarily by land use, distance from Stockholm, and slope. It differentiated forested municipalities with higher slopes of the Northwest part of the basin from the flatter agricultural land located around lakes Mälaren and Hjälmaren (Fig. 3c). Without considering covariation among ecosystem services, the drivers explain 59% of the variation among ecosystem services across municipalities. To see the random forest analysis ranking of all the drivers see Figure A4.1
There were large differences regarding how well the different candidate models predicted individual ecosystem services. (Fig. 4). Some services, e.g., forest products, cross-country skiing, and biodiversity appreciation, were strongly predicted by all candidate models, including the reference model. Others, e.g., wheat production, cattle, and pollination, were predicted well by one candidate model, Land Use, and the reference model. Finally, a number of services, e.g., pigs, N-retention, P-retention, outdoor recreation, moose hunting, and summer cottages, were poorly predicted by all five models.
Overall, our results show that the reference model performed best in predicting the different ecosystem services. Land Use was the candidate model that best predicted individual ecosystem services (highest adjusted R² for 8 out of 16 services). Specifically, this model had the highest performance for predicting all provisioning services assessed in this study, three of the regulating services (pollination, standing water quality and running water quality), and one cultural service (cross-country skiing). Nevertheless, some regulating services and the majority of cultural services were clearly not well predicted by land use drivers. The Location Theory model was best predicting N and P retention and horseback riding, although these were better predicted by the reference model. The two candidate models that were based on socioeconomical drivers, Ecological Modernization and Ecological Footprint, were only able to better predict a few services. The Ecological Modernization model predicted the distribution of moose hunting and summer cottages relatively better, while the Ecological Footprint model predicted biodiversity appreciation better. Importantly, while relatively better predicted by the Ecological Modernization model, summer cottages and moose hunting were not well predicted by any of the sets of drivers included in this study.
The overall capacity of the different models for predicting individual ecosystem services within each municipality is shown in Figure 5, given by an R² by municipality (darker tones of green indicate higher R² and thus a better performance of the model). An overall R² for all municipalities is also shown in the figure. Although the reference model had the highest ability to predict the distribution of individual services (R² = 0.79), the Land Use model performed almost as well (R² = 0.72). The Ecological Modernization model and the Ecological Footprint model had the lowest capacity for explaining the data variability (R² = 0.60 and R² = 0.61, respectively). The figure shows that none of the assessed models had the ability to predict the distribution of individual services well in the urban municipalities close to Stockholm.
The hierarchical partitioning analysis showed that the ability to predict ecosystem services was shared among variables, and that different ecosystem services were best predicted by different variables (see Fig. A5.1). Land variables did well at predicting agricultural ecosystem services, and log population density, log wealth, and distance to Stockholm did well at predicting many other ecosystem services. No variables predicted outdoor recreation, and with the exception of built-up land being a good predictor of cross-country skiing, many variables were weak predictors of most cultural services.
We used the same candidate models to predict the spatial distribution of ecosystem service bundles across the basin (Fig. 5). The results for the variation of bundles predicted by the different models, through two different methods, are shown as Figs. 5b and 5c. Although the first (Fig. 5b) only measured if bundles were rightly or wrongly predicted in each of the municipalities, the second gave a measure of how much of the bundles variation within each municipality is explained by the different models, generating a gradient of accuracy from low to high predictability (Fig. 5c). The Land Use model equaled the reference model for the highest accuracy in predicting the match or mismatch between the predicted and the actual distribution of bundles across the basin (R² = 0.73), a significantly better performance than the other three models (Fig. 5b). All tested models had a low ability for predicting the differentiation between the distribution of the two agricultural bundles, Mosaic crop-livestock and Mosaic crop-horses, which included most municipalities around lakes Mälaren and Hjälmaren. These municipalities were also the ones that presented higher multifunctionality, which indicates higher complexity (Fig. 1; Queiroz et al. 2015). When considering the gradient of accuracy (from low to high predictability depending on the amount of bundles variation explained by each model), the models based on socioeconomic variables performed better than the Land Use model (Fig. 5c). The Ecological Footprint model did best (R² = 0.58) followed by Location Theory (R² = 0.56). These both performed better than the reference model (R² = 0.55). Both methods showed that none of the models were able to predict the distribution of ecosystem service bundles for the Stockholm municipalities well (urban bundle group), which had a high expression of cultural services.
Unexplained spatial autocorrelation was found among the residuals for almost all ecosystem services for all models, for both bundles and individual ecosystem services. The pervasiveness of autocorrelation suggests that there are spatial patterns in the region and that the models are not accounting for. These are exploratory models are they are not able to account for all the spatial variation. This indicates that other drivers are needed to be included in the models or there needs to be some type of spatial interaction included.
The distribution of both individual services and bundles of ecosystem services across a human-dominated landscape can be well predicted by sets of relatively few drivers based on publicly available data (Fig. 5). There is a cost to data, and the ability to create understanding about the ecosystem service context using limited variables is important particularly in data scarce regions. We found that the reference model that combined all drivers, using social, ecological, and geographical variables together, performed better than the models based on one single theory or set of drivers alone. These findings suggest that ecosystem service assessments could be improved by adopting a more social-ecological approach that integrates multiple social, geographical, and ecological theories, and utilizes diverse types of data.
Several of our results suggest that integrating social-ecological interactions may simplify rather than complicate the analysis of ecosystem services. First, we found that by integrating different models and variables we can predict a substantial amount of the observed variation among services. Second, our results showed the existence of strong correlations among different types of drivers, such as the correlation between income, wealth, built-up land, and distance to Stockholm. This suggests that different complementary sets of drivers can capture large proportions of social-ecological variation. Third, using ecosystem service bundles to predict average values of individual services was enough to capture a substantial variation of ecosystem service patterns across the landscape. These three findings suggest that new social-ecological models of how different types of social, geographic, and ecological drivers are related to the distribution of ecosystem services may offer rapid and reasonably accurate ways to assess their distribution across landscapes.
However, although predicting the distribution of individual ecosystem services using bundles worked reasonably well, this was not the case in urban areas. Cultural ecosystem services are one of the defining features of urban areas, and the urban bundle in this landscape. One potential explanation for the inability of the models to successfully predict the bundles for these municipalities is that the drivers included in this study performed relatively poorly in predicting cultural ecosystem services. Also, the delineation between bundles of ecosystem services in the Norrström drainage basin is less distinct (Fig. 3c) than that of other regions where similar analyses have been done (Raudsepp-Hearne et al. 2010, Turner et al. 2014). This lack of clear boundaries between bundles is the reason behind the discrepancy between the results obtained with the method used in Fig. 5b and those obtained using the voting method described in Fig. 5c. Although the models failed to correctly predict bundles for many of the municipalities in Fig. 5b, predictions were still quite accurate with the weighted average of the votes for different bundles of ecosystem services (Fig. 5c). Predicting bundles of ecosystem services is a fairly quick and simple strategy for understanding the interaction among services and inferring something about the social-ecological context of an area. However, this method is not likely to be useful unless bundles of ecosystem services are more clearly defined within a landscape. Still, even in those cases where bundles are not very distinct, to explore the relationship between services, for example, by correlation analysis, is always a useful approach and avoids some of the problems of single service approaches. Future work should test which approach, bundles or interactions between individual services, is more useful in different landscapes.
Our results partially support the use of land use as an integrated social-ecological indicator of ecosystem services, especially provisioning services, as it has been used in previous studies (e.g., Costanza and Folke 1997, Chan et al. 2006). However, we highlight certain limits to this approach. For example, the Land Use model poorly predicted a number of regulating and cultural services. Specifically, two cultural services that could be thought of as closely tied to land use, horseback riding and moose hunting, were best predicted by the Location Theory model and the Ecological Modernization model, respectively. This suggests that the reliance on land use as an indicator of ecosystem services may miss some types of regulating and cultural services.
Despite success in predicting ecosystem services, there is a lack of a clear theory for explaining the production of observed patterns of ecosystem services. No individual candidate model worked consistently well, and analysis of the relative importance of different variables revealed that a mix of land use and geographical variables were important independent predictors. Furthermore, several recreation services, outdoor recreation and summer cottages, were poorly predicted by all models. Also, the analysis of model residuals showed that significant spatial correlation remains, demonstrating that important variables are missing. Some part of the unexplained variance is due to things like the geology of the Norrström, which was not captured in the drivers used for this study. For example, the proximity of certain municipalities to the Baltic and major rivers results in rapid nutrient transportation from inland to the Baltic Sea, not allowing for high nutrient retention. In this case, biophysical and topographical characteristics of the landscape are key elements for understanding the interactions between services. However, we are less certain about what driver variables would be needed to better predict certain services, like recreation. Most cultural services are the result of the direct experience of nature by humans and can therefore depend on more intangible factors such as individual human preferences (Martin-López et al. 2012, Daniel et al. 2012). Therefore, we suggest that there is a need for ecosystem service research to develop better integrated social-ecological conceptual models that explain the distribution of ecosystem services in landscapes, and that such theory creation could be informed by theory from geography and other social sciences, especially those that analyze recreation and other cultural uses of landscapes and ecosystems.
The distribution of individual services and bundles of ecosystem services across the Norrström basin was strongly linked to land use and urbanization (Fig. 3a-c). This region is one of the fastest growing urban areas in Europe (Sporrong 2008), and that explains the importance of Stockholm as a determinant of ES distribution across the study region. One of the striking features of ES in this region is the high level of many services in and near the densely populated city of Stockholm. This might be related with something intrinsic to Swedish culture, where the connection and access to nature assumes a central role for most Swedes (Gidlöf-Gunnarsson and Öhrström 2007) or some other factors, but our knowledge of what drives the production of cultural services in urban areas is so far poor. A better understanding on what drives these patterns would be useful for future planning in the region, especially identifying how to maintain and enhance these services during its rapid population and urban growth. Our results suggest that land use, and consequently land conversion, and the regional connections to Stockholm are key forces shaping the distribution of ecosystem services. However, there are substantial unknowns about what is determining the distribution of recreation activities and that deserves further investigation. We hope that these results can be developed in further studies to provide some tractable tools to aid in understanding, discussing, and planning for ecosystem services in this rapidly urbanizing region. We expect that one of the most useful ways of achieving this task will be to compare Stockholm region to other regions in Sweden and the world.
The approach presented here bridges modeling of ecosystem services that is based on theoretical assumptions, but may not be empirically based, with mapping that is not theoretically based and is not included in modeling (see Table A6.1). Comparison among multiple regions is essential to develop a richer, generalizable understanding of the distribution of multiple ecosystem services across landscapes. We argue that because multiple social-ecological factors appear to codetermine the distribution of ecosystem services, there is a need to develop an explicitly social-ecological theory of what predicts the pattern of ecosystem services. This theory should be developed and tested in multiple locations to identify what aspects of ecosystem services are unique and idiosyncratic and which relationships are general. It would also be useful to consider if analyses using indicators that are not spatially linked would reflect similar results. It would be possible to test provisioning services, for example, economically. Furthermore, to understand how social and ecological changes shape the availability of ecosystem services it is necessary to test how the supply of ecosystem services changes over time. We argue that the empirical analysis of patterns is an important complement to the development of process-based models and expert knowledge-driven models of ecosystem services. We propose that identifying better theories and variables to predict the supply of regulating and cultural services, and applying and extending the statistical approaches presented in this paper to other regions in which multiple ecosystem services have been assessed, would be two useful and productive next steps to understand what types of factors predict the distribution of multiple ecosystem services.
This research was conducted under the Social-ecological dynamics of ecosystem services in the Norrström basin (SEEN) project, financed by the Swedish Research Council Formas (project number 2012-1058) as well as the Strategic Research Program EkoKlim at Stockholm University. This research contributes to the Program on Ecosystem Change and Society (www.pecs-science.org).
Akaike, H. 1974. A new look at the statistical model identification. Automatic Control, IEEE Transactions 19(6):716-723. http://dx.doi.org/10.1109/TAC.1974.1100705
Alonso, W. 1964. Location and land use. Toward a general theory of land rent. Harvard University Press, Cambridge, Massachusetts, USA. http://dx.doi.org/10.4159/harvard.9780674730854
Bennett, E. M., G. D. Peterson, and L. J. Gordon. 2009. Understanding relationships among multiple ecosystem services. Ecology Letters 12(12):1394-1404. http://dx.doi.org/10.1111/j.1461-0248.2009.01387.x
Bivand, R. S., E. Pebesma, and V. Gómez-Rubio. 2013. Applied spatial data analysis with R. Second edition. Springer, New York, New York, USA. http://dx.doi.org/10.1007/978-1-4614-7618-4
Briassoulis H. 2000. Analysis of land use change: theoretical and modeling approaches. In S. Loveridge and R. W. Jackson, editors. The web book of regional science. West Virginia University, Morgantown, Virginia, USA. [online] URL: http://www.rri.wvu.edu/WebBook/Briassoulis/contents.htm
Burkhard, B., F. Kroll, F. Müller, and W. Windhorst. 2009. Landscapes’ capacities to provide ecosystem services—a concept for land-cover based assessments. Landscape Online 15:1-22.
Burnham, K. P., and D. R. Anderson. 2004. Multimodel inference understanding AIC and BIC in model selection. Sociological Methods & Research 33(2):261-304. http://dx.doi.org/10.1177/0049124104268644
Carpenter, S. R., H. A. Mooney, J. Agard, D. Capistrano, R. S. DeFries, S. Díaz, T. Dietz, A. K. Duraiappah, A. Oten-Yeboah, H. M. Pereira, C. Perrings, W. V. Reid, J. Sarukhan, R. J. Scholes, and A. Whyte. 2009. Science for managing ecosystem services: beyond the Millennium Ecosystem Assessment. Proceedings of the National Academy of Sciences 106(5):1305-1312. http://dx.doi.org/10.1073/pnas.0808772106
Chan, K. M. A., M. R. Shaw, D. R. Cameron, E. C. Underwood, and G. C. Daily. 2006. Conservation planning for ecosystem services. PLoS Biology 4(11):e379. http://dx.doi.org/10.1371/journal.pbio.0040379
Chertow, M. R. 2000. The IPAT equation and its variants. Journal of Industrial Ecology 4(4):13-29. http://dx.doi.org/10.1162/10881980052541927
Chevan, A., and M. Sutherland. 1991. Hierarchical partitioning. American Statistician 45:90-96.
Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R. V. O’Neill, J. Paruelo, R. G. Raskin, P. Sutton, and M. van den Belt. 1997. The value of the world’s ecosystem services and natural capital. Nature 387(15):253-260. http://dx.doi.org/10.1038/387253a0
Costanza, R., and C. Folke. 1997. Valuing ecosystem services with efficiency, fairness and sustainability as goals. Nature’s Services: Societal Dependence on Natural Ecosystems 49-70.
Cutler, D. R., T. C. Edwards Jr, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler. 2007. Random forests for classification in ecology. Ecology 88(11):2783-2792. http://dx.doi.org/10.1890/07-0539.1
Daily, G. C., S. Polasky, J. Goldstein, P. M. Kareiva, H. A. Mooney, L. Pejchar, T. H. Ricketts, J. Salzman, and R. Shallenberger. 2009. Ecosystem services in decision making: time to deliver. Frontiers in Ecology and the Environment 7:21-28. http://dx.doi.org/10.1890/080025
Daniel, T. C., A. Muhar, A. Arnberger, O. Aznar, J. W. Boyd, K. M. A. Chan, R. Costanza, T. Elmqvist, C. G. Flint, P. H. Gobster, A. Grêt-Regamey, R. Lave, S. Muhar, M. Penker, R. G. Ribe, T. Schauppenlehner, T. Sikor, I. Soloviy, M. Spierenburg, K. Taczanowska, J. Tam, and A. von der Dunk. 2012. Contributions of cultural services to the ecosystem services agenda. Proceedings of the National Academy of Sciences 109(23):8812-8819. http://dx.doi.org/10.1073/pnas.1114773109
de Groot, R. S., R. Alkemade, L. Braat, L. Hein, and L. Willemen. 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity 7(3):260-272. http://dx.doi.org/10.1016/j.ecocom.2009.10.006
Dietz, T., E. A. Rosa, and R. York. 2007. Driving the human ecological footprint. Frontiers in Ecology and the Environment 5(1):13-18. http://dx.doi.org/10.1890/1540-9295(2007)5[13:dthef]2.0.co;2
Dormann, C. F., J. M. McPherson, M. B. Araújo, R. Bivand, J. Bolliger , G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. D. Kissling, I. Kühn, R. Ohlemüller, P. R. Peres-Neto, B. Reineking, B. Schröder, F. M. Schurr, and R. Wilson. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609-628. http://dx.doi.org/10.1111/j.2007.0906-7590.05171.x
Dray, S., and A. B. Dufour. 2007. The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22(4):1-20. http://dx.doi.org/10.18637/jss.v022.i04
Gidlöf-Gunnarsson, A., and E. Öhrström. 2007. Noise and well-being in urban residential environments: the potential role of perceived availability to nearby green areas. Landscape and Urban Planning 83:115-126. http://dx.doi.org/10.1016/j.landurbplan.2007.03.003
Griffith, D. A. 1999. Statistical and mathematical sources of regional science theory: map pattern analysis as an example. Papers in Regional Science 78(1):21-45.
Grossman, G. M., and A. B Krueger. 1995. Economic growth and the environment. Quarterly Journal of Economics 110(2):353-377. http://dx.doi.org/10.2307/2118443
Hanspach, J., T. Hartel, A. I. Milcu, F. Mikulcak, I. Dorresteijn, J. Loos, H. von Wehrden, T. Kuemmerle, D. Abson, A. Kovács-Hostyánszki , A. Báld, and J. Fischer. 2014. A holistic approach to studying social-ecological systems and its application to southern Transylvania. Ecology and Society 19(4):32. http://dx.doi.org/10.5751/es-06915-190432
Harrison, P., and F. Pearce. 2000. AAAS atlas of population & environment. University of California Press, Berkeley, California, USA.
Hersperger, A. M., M. Gennaio, P. H. Verburg, and M. Bürgi. 2010. Linking land change with driving forces and actors: four conceptual models. Ecology and Society 15(4):1. [online] URL: http://www.ecologyandsociety.org/vol15/iss4/art1/
Jorgenson, A. K. 2003. Consumption and environmental degradation: a cross-national analysis of the ecological footprint. Social Problems 50:374-394. http://dx.doi.org/10.1525/sp.2003.50.3.374
Kuznets, S. 1955. Economic growth and income inequality. American Economic Review 45:1-28.
Lambin, E. F., H. Geist, and R. R. Rindfuss. 2006. Introduction: local processes with global impacts. Pages 1-8 in E. F. Lambin and H. Geist, editors. Land-use and land-cover change: local processes and global impacts. Springer, Berlin, Germany. http://dx.doi.org/10.1007/3-540-32202-7_1
Liaw, A., and M. Wiener. 2002. Classification and regression by randomForest. R News 2(3):18-22.
MacNally, R. 2002. Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodiversity and Conservation 11:1397-1401. http://dx.doi.org/10.1023/A:1016250716679
MacNally, R., and C. J. Walsh. 2004. Hierarchical partitioning public-domain software. Biodiversity and Conservation 13:659-660. http://dx.doi.org/10.1023/B:BIOC.0000009515.11717.0b
Martín-López, B., I. Iniesta-Arandia, M. García-Llorente, I. Palomo, I. Casado-Arzuaga, D. G. Del Amo, E. Gómez-Baggethun, E. Oteros-Rozas, I. Palacios-Agundez, B. Willaarts, J. A. González, F. Santos-Martín, M. Onaindia, C. López-Santiago, C. Montes. 2012. Uncovering ecosystem service bundles through social preferences. Plos ONE 7:e38970. http://dx.doi.org/10.1371/journal.pone.0038970
Mol, A. P. 2003. Globalization and environmental reform: the ecological modernization of the global economy. MIT Press, Cambridge, Massachusetts, USA.
Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D. R. Cameron, K. M. A. Chan, G. C. Daily, J. Goldstein, P. M. Kareiva, E. Lonsdorf, R. Naidoo, T. H. Ricketts, and M. R. Shaw. 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment 7:4-11. http://dx.doi.org/10.1890/080023
Nelson, E., S. Polasky, D. J. Lewis, A. J. Plantinga, E. Lonsdorf, D. White, D. Bael, and J. J. Lawler. 2008. Efficiency of incentives to jointly increase carbon sequestration and species conservation on a landscape. Proceedings of the National Academy of Sciences 105:9471-9476. http://dx.doi.org/10.1073/pnas.0706178105
Prasad, A. M., L. R. Iverson, and A. Liaw. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2):181-199. http://dx.doi.org/10.1007/s10021-005-0054-1
Putz, F. E., and C. Romero. 2001. Biologists and timber certification. Conservation Biology 15:313-314. http://dx.doi.org/10.1046/j.1523-1739.2001.015002313.x
Queiroz, C., M. Meacham, K. Richter, K., A. V. Norström, E. Andersson, J. Norberg, and G. Peterson. 2015. Mapping bundles of ecosystem services reveals distinct types of multifunctionality within a Swedish landscape. Ambio 44(1):89-101. http://dx.doi.org/10.1007/s13280-014-0601-0
Raudsepp-Hearne, C., G. D. Peterson, and E. M. Bennett. 2010. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proceedings of the National Academy of Sciences 107(11):5242-5247. http://dx.doi.org/10.1073/pnas.0907284107
R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: https://www.r-project.org/
Reyers, B., R. Biggs, G. S. Cumming, T. Elmqvist, A. P. Hejnowicz, and S. Polasky. 2013. Getting the measure of ecosystem services: a social-ecological approach. Frontiers in Ecology and the Environment 11(5):268-273. http://dx.doi.org/10.1890/120144
Schröter, M., R. P. Remme, and L. Hein. 2012. How and where to map supply and demand of ecosystem services for policy-relevant outcomes? Ecological Indicators 23:220-221. http://dx.doi.org/10.1016/j.ecolind.2012.03.025
Sporrong, U. 2008. Features of Nordic physical landscapes: regional characteristics. Pages 568-585 in K. R. Olwig and M. Jones, editors. Nordic landscapes: region and belonging on the northern edge of Europe. University of Minnesota Press, Minneapolis, Minnesota, USA.
Stern, P. C., O. R. Young, and D. Druckman, editors. 1992. Global environmental change: understanding the human dimensions. National Academies Press, Washington, D. C., USA.
Tilman, D., K. G. Cassman, P. A. Matson, R. Naylor, and S. Polasky. 2002. Agricultural sustainability and intensive production practices. Nature 418:671-677. http://dx.doi.org/10.1038/nature01014
Turner, K. G., M. V. Odgaard, P. K. Bøcher, T. Dalgaard, and J.-C. Svenning. 2014. Bundling ecosystem services in Denmark: trade-offs and synergies in a cultural landscape. Landscape and Urban Planning 125:89-104. http://dx.doi.org/10.1016/j.landurbplan.2014.02.007
von Thünen, J. R. 1826. The isolated state. English edition 1966. Pergainon, Oxford, UK.
Wickham H. 2009. ggplot2: elegant graphics for data analysis. Springer, New York, New York, USA.
York, R., E. A. Rosa, and T. Dietz. 2003. Footprints on the earth: the environmental consequences of modernity. American Sociological Review 68:279-300. http://dx.doi.org/10.2307/1519769
Zhang, W., T. H. Ricketts, C. Kremen, K. Carney, and S. M. Swinton. 2007. Ecosystem services and dis-services to agriculture. Ecological Economics 64:253-260. http://dx.doi.org/10.1016/j.ecolecon.2007.02.024