Developing and applying ecosystem service ...

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/283066134

Developing and applying ecosystem service indicators in decision-support at various scales Article in Ecological Indicators · October 2015 DOI: 10.1016/j.ecolind.2015.09.037

CITATIONS

READS

2

399

7 authors, including: Christine Fürst

Daniele La Rosa

Martin Luther University Halle-Wittenberg

University of Catania

65 PUBLICATIONS 793 CITATIONS

42 PUBLICATIONS 181 CITATIONS

SEE PROFILE

SEE PROFILE

Carsten Lorz Hochschule Weihenstephan-Triesdorf 93 PUBLICATIONS 569 CITATIONS SEE PROFILE

Some of the authors of this publication are also working on these related projects:

Sustainable Landscape Development View project COST Action TU0902 "Integrated assessment technologies to support the sustainable development of urban areas" View project

All content following this page was uploaded by Jennifer Hauck on 19 November 2015. The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.

G Model

ARTICLE IN PRESS

ECOIND-2652; No. of Pages 5

Ecological Indicators xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Special Issue Editorial

Developing and applying ecosystem service indicators in decision-support at various scales

1. Introduction On-going processes, such as the work of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES, Larigauderie and Mooney, 2010) and the implementation of the EU Biodiversity Strategy (e.g. Maes et al., 2014) show that ecosystem services (ES) have not gained only high perception in research, but have meanwhile reached the status of a concept for supporting policies, programs and plans. However, the gap between theory and implementation in planning and decision making concerning (ES) is still wide, as proven by the number of scientific papers that address how to move from scientific knowledge about ES to realworld decision making (e.g. Daily et al., 2009; Bastian et al., 2012; Hauck et al., 2013; Ruckelshaus et al., 2013; Albert et al., 2014). Many ES indicators have been developed, e.g. Van Reeth (2013) found over a hundred ES indicators for a case study on the regional level in Belgium alone. Researchers continue to develop indicators or even indicator frameworks for decision making (e.g. Burkhard et al., 2012; Koschke et al., 2012; van Oudenhoven et al., 2012; Kandziora et al., 2013; Turkelboom et al., 2013; Helfenstein and Kienast, 2014; Maes et al., 2014; UK NEA, 2011, http://uknea.unepwcmc.org/; Albert et al., 2015a). However, according to Burkhard et al. (2012), the different approaches illustrate that the definition of a general classification framework remains a major challenge, because ES studies are rather singular, question-dependent and context related. Another reason for the gap might be that most of these indicators, or sets of indicators, have only been tested in scientific studies alone, but more often than not still need to be applied in real world decision making (e.g. Bagstad et al., 2013). Therefore, most recent methods such as balanced score cards are suggested to assess how valuable and practicable ES have been to support and improve decision making (Fürst et al., 2014). Despite these issues, national governments started to conduct continuous assessments of biodiversity and ES (Ruckelshaus et al., 2013). Among the main problems encountered, there is the lack of mutual agreement about what indicators are (Heink and Kowarik, 2010), the complexity they address (Turnhout et al., 2007), how they have to be designed (Failing and Gregory, 2003) and the mismatch between their scale, and the decision-making scale (Dick et al., 2014). 2. Objective In order to help to bridge the science-policy gap, this special issue assembles contributions concerning the development and

application of ES indicators in policy and decision making processes at various scales, contexts and cultural settings. The objective is to advance the understanding of the requirements for ES indicators in decision making. Moreover, this special issue discusses different forms of stakeholder involvement and related social processes of how ES indicators are defined, assessed, and communicated to provide useful decision support for policy, planning and management. The individual contributions to the special issue provide insights into the following key themes of ES indicator development and application: • • • • • •

Ambiguities of the ES concept Addressing the issues of scales Requirements for policy-relevant ES indicators Conceptual frameworks for ES indicator development Normative bias of indicator selection and interpretation Transdisciplinarity

2.1. Ambiguities of the ES concept Ambiguities of the ES concept, as pointed out by Heink et al. (2015) and Albert et al. (2015a) can, for example, be related to measuring the potential or actual use of ES. Another ambiguity concerns the question if indicators cover only the natural capitals in the delivery of ES or also include other capitals, such as machines, skills, and labour. A further questions concerns which ES values are included. For instance, often monetary values are promoted on the expense of non-monetary values. Ambiguities also revolve around the question of which phenomena are included in the ES concept. For example ecosystem disservices, i.e. “functions of ecosystems that are perceived as negative for human well-being” (Lyytimäki and Sipilä, 2009: 311) are rarely included in general and consequently in indicator discussions in particular (Haase et al., 2014; La Rosa et al., 2015). The ambiguities can cause considerable confusion, particularly for those people that have limited ES literacy, i.e. less familiarity with the ES concept and its benefits and shortcomings. This can lead to reservations, e.g. to use of economic valuation approaches (Schröter et al., 2014). Furthermore, it can lead to scepticism concerning the usefulness of ES indicators in (biodiversity) conservation contexts (e.g. Saarela and Rinne, 2015) or more generally in planning and decision making (Fürst et al., 2012). It can, however, also lead to enthusiasm ignoring some of the shortcoming associated with the ES concept (Albert et al., 2014).

http://dx.doi.org/10.1016/j.ecolind.2015.09.037 1470-160X/© 2015 Published by Elsevier Ltd.

Please cite this article in press as: Hauck, J., et al., Developing and applying ecosystem service indicators in decision-support at various scales. Ecol. Indicat. (2015), http://dx.doi.org/10.1016/j.ecolind.2015.09.037

G Model ECOIND-2652; No. of Pages 5

ARTICLE IN PRESS Special Issue Editorial / Ecological Indicators xxx (2015) xxx–xxx

2

2.2. Addressing the issues of scales Many contributors of the special issue (e.g. Pelorosso et al., 2015; Norton et al., 2015; Tratalos et al., 2015; La Rosa et al., 2015) identify scale-mismatches, i.e. indicators and associated (available) data sets do not fit the specific scale of decision making. This becomes more critical when decision making processes span across scales, or beyond national borders (Fürst et al., 2010; Spyra, 2014). Considering multiple scales in decision making becomes important, because ES are not only consumed at one scale but at multiple scales (La Rosa et al., 2015) and because this allows accounting for trade-offs between ES provision for different uses (Albert et al., 2015b). Therefore ES indicators require a multi-level governance approach taking into consideration international classifications like the Common International Classification of ES framework (CICES, Haines-Young and Potschin, 2010) and policy contexts such as the IPBES. However, indicators also need to fit to specific biodiversity policy context, for example in the EU that have to be consistent with the framework suggested for implementation of Action 5 of the Biodiversity Strategy (Maes et al., 2014). Furthermore, many decisions for the conservation and sustainable use of ES and biodiversity need to be made on the national and more importantly on the regional and local level. Indicators to support decision making processes, particularly on these two lower levels, still need to be in line with those on the levels above, but have different requirements e.g. concerning accuracy and detail (Norton et al., 2015; Albert et al., 2015b). Using the example of cultural ecosystem services (CES), Tratalos et al. (2015) show possible approaches for dealing with scalar issues. One approach includes assessing CES only at local scale. Alternatively the authors recommend investigating whether cost effective indicators, using currently available data, could be established. Although the resulting set of indicators may not have the depth, they could still be relevant on a national level, e.g. for contributing to ES literacy; showing areas of particular importance e.g. to create new protected areas; revealing trends in the effects of landscape change on CES provision; improving the understanding of the relationship between local provision of CES and the current demand for them between different locations. 2.3. Requirements for policy-relevant ES indicators While there seems to be a general consensus about the necessity to resolve conceptual ambiguities and pay attention to scale issues, this is often in contrast to questions of ES indicators practicality. Diehl et al. (2015) for example found that many discussions in the ES context resulted in its perception of an altogether too complex framework for decision making. For decision makers, indicators need to be: easy to understand (e.g. in monetary terms), widely applicable, cost-effective, valid over time and space, i.e. preferably coverable with data that are already collected for other purposes, and may stand legal challenge in negotiations. In other words, decision makers require ES indicators that are legitimate (Fürst et al., 2013; Mononen et al., 2015; Albert et al., 2015a,b; La Rosa et al., 2015; Tratalos et al., 2015; Diehl et al., 2015; Heink et al., 2015; Saarela and Rinne, 2015). While these are valid considerations, fulfilling these criteria can lead to oversimplification and biased debate on economic significance of ES, ignoring other values (Mononen et al., 2015). This concerns particularly CES leading to a prominent lack of indicators beyond recreation and tourism and landscape aesthetics (La Rosa et al., 2015; Tratalos et al., 2015; Heink et al., 2015). Apart from the criteria posed by decision makers, scientific quality criteria should not be sidelined. The validity of indicators, i.e. the extent an indicator represents the subject to be indicated, should also be considered as a crucial part of the scientific credibility (Heink et al., 2015; Saarela and Rinne, 2015). This is particularly true

in cases of proxy indicators, i.e. substitute measures used to provide insight into the area of interest when it is not possible to measure the issue directly but still are reasonably synchronous with a good direct measure (ESID, 2012). This discussion is particularly relevant in the context of cultural ES, where the challenge is to find quantitative indicators able to express the cultural dimension of specific ES in a spatially-explicit way (La Rosa et al., 2015; Tratalos et al., 2015). Innovative tools, like participatory GIS tools, such as smart phone applications, could be used for example in the context of Citizen Science to assess, in spatially explicit way, the actual use, i.e. demand for CES (Priess et al., 2014; Frank et al., 2015). 2.4. Conceptual frameworks for ES indicator development To strike a balance between practicability and oversimplification, and deal with other challenges mentioned a number of authors found conceptual frameworks helpful (e.g. Albert et al., 2015b; Mononen et al., 2015; La Rosa et al., 2015; Diehl et al., 2015; Wissen Hayek et al., 2015). Mononen et al. (2015), and Diehl et al. (2015) suggest using the “ecosystem service cascade” proposed by Potschin and Haines-Young (2011). Mononen et al. (2015) propose to use four indicators for each step in the cascade and for each ES to cover the whole delivery process from ecosystem structure to values. Benefits of such an approach would be an increased understanding of the topic by presenting clear, systematic, and qualitative information and a common technical language (Mononen et al., 2015; Diehl et al., 2015). Apart from a clear structuring, the use of such a blueprint would also enable comparisons between studies for decision making and monitoring on higher levels, such as the European Union (Diehl et al., 2015). Albert et al. (2015b) suggest an alternative approach. The authors suggest to adapt the ES concept to current conceptual models of decision making rather than the other way around. At the example of landscape planning, the authors introduce an ES-inPlanning framework, which combines ES assessment and valuation indicators with the widely used Driving Forces, Pressures, State, Impacts and Responses (DPSIR) model. In their framework, ES indicators become part of landscape planning as a means of assessing the current state of the environment and for determining how it might change in the future. 2.5. Normative bias of indicator selection and interpretation Mononen et al. (2015), Diehl et al. (2015), and Saarela and Rinne (2015) point out that indicators are not only a means to structure and communicate information, but also the result of politically normative decisions on what is important. In other words: what is considered as relevant for analysis and the justification of an indicator is context sensitive and depends on the norms and customs of the actors selecting indicators (Diehl et al., 2015; Saarela and Rinne, 2015). This turns indicator selection into a political process which is likely to be challenged by stakeholders, who might think that their agenda is not properly implemented, in the worst case causing battle of impact assessments (Diehl et al., 2015). Concerns to create such situations may seriously hamper discussions concerning to ES indicators. For example, in the case provided by Saarela and Rinne (2015) assessments where only allowed for land use planning situations where mutual, policy-level understanding and agreement existed. Assessments in contested situations were not carried out due to concerns to complicate future processes. 2.6. Transdisciplinarity A crucial component of any approach to developing and applying ES indicators in decision-support is transdisciplinarity. Transdisciplinarity is here defined following Lang et al. (2012) as

Please cite this article in press as: Hauck, J., et al., Developing and applying ecosystem service indicators in decision-support at various scales. Ecol. Indicat. (2015), http://dx.doi.org/10.1016/j.ecolind.2015.09.037

G Model ECOIND-2652; No. of Pages 5

ARTICLE IN PRESS Special Issue Editorial / Ecological Indicators xxx (2015) xxx–xxx

a reflexive, integrative, method-driven scientific principle aiming at the solution or transition of societal problems and concurrently of related scientific problems by differentiating and integrating knowledge from various scientific and societal bodies of knowledge. The contributions to this special issue make a number of recommendations on how to design transdisciplinary research to improve decisions on the ground based on ES indicators. Based on direct feedback from their stakeholders, Saarela and Rinne (2015) and Wissen Hayek et al. (2015) both recommend working on real-world problems in pilot study settings and using flexible prototypes, e.g. of platforms, models or conceptual frameworks that can be adjusted. According to Saarela and Rinne (2015) and Wissen Hayek et al. (2015) a direct link to an ongoing planning process was seen as a significant reason for the successful application of the ES indicators, in terms of learning, knowledge production, dialogue between different jurisdictions, and making abstract approaches like ES meaningful. Learning, developing a mutual understanding and hence the ability to express concrete demands, e.g. to elicit preferred ways of valuing ES or for indicators and associated requirements can be greatly enhanced when iteration is a substantial part of the research design (Ruckelshaus et al., 2013; Mononen et al., 2015; Wissen Hayek et al., 2015; Saarela and Rinne, 2015). In their study about the “Collaborative Development of a Web-based Visualization Platform”, Wissen Hayek et al. (2015) describe a situation, where in the beginning of the project, the practice actors were not able to clearly define their requirements and expectations. For this reason, the purpose of the ES indicators and the target group of the visualization platform were defined rather broadly. The prototypes turned out as effective media for successively defining the contents and the functionality of the visualization platform including ES indicators. Bearing in mind that a learning process is required to specify the expected outcome, it should be iteratively reflected throughout the collaboration process and based on concrete indicator examples, what the purpose of the ES indicators is in practice. Iteration is also a means to integrate different types of knowledge. In their contribution, Norgrove and Hauser (2015) analyze indicators which farmers use in West and Central Africa to determine when to re-cultivate a fallow. In doing so, the farmers use knowledge relying probably on the experiences of hundreds of years. Their research demonstrates the relative paucity of documentation of farmers’ knowledge in the region yet confirms the quality of ecological knowledge used. In their Finnish example, the stakeholders included in Saarela and Rinne’s (2015) case study likewise stated that researchers and civil servants felt that it was necessary to combine researchers’ interpretations with local knowledge and apprehension of the civil servants, acquired during their long work experience. This kind of approach not only allows co-producing knowledge but, according to Saarela and Rinne (2015) also allows the co-interpretation of results to achieve maximum usefulness. In order to realize the benefits of transdisciplinarity, the research process needs to meet certain requirements. Openmindedness is key to accept and deal with the concerns both from decision makers as well as from the scientists. Open-mindedness and having the time to arrive at a mutual understanding of the conceptual basis of the ES indicators were considered essential to establish an atmosphere of trust and commitment between the researchers and civil servants as well as between civil servants from different departments in the case described by Saarela and Rinne (2015). Transparency is essential here too, for example when selecting the stakeholders, who participate in the process (Wissen Hayek et al., 2015). Wissen Hayek et al. (2015) recommend a so-called functional-dynamic organization of transdisciplinary processes, suggesting a targeted collaboration with different actor groups applying involvement techniques, rather than involving all

3

of the participating actors with high intensity (Stauffacher et al., 2012). Thus, organizing these processes needs certain skill sets and adequate knowledge (Turnhout et al., 2013; Wissen Hayek et al., 2015; Saarela and Rinne, 2015). Wissen Hayek et al. (2015) point to management skills to carry out iterative communication and specification process. Saarela and Rinne (2015) point to the ‘bridging’ and ‘supplying’ knowledge brokering repertoires identified by Turnhout et al. (2013) to facilitate the flows of knowledge and discussions among stakeholders (including scientists). Further, boundary objects are found to be useful. In our special issue ES indicators can be considered boundary objects in so far as they provide a vehicle to facilitate discussions by making abstract contents more concrete and to connect different actors to jointly address an environmental issue of concern (Opdam et al., 2015). However, Saarela and Rinne (2015) also point out the necessity to acknowledge that ES indicators are not ‘neutral’ boundary objects, but instead the discussions they generate are tied to the values and perceptions of both producers and users of the ES indicators. Pelorosso et al. (2015) demonstrate the value of the ES concept also for interdisciplinary collaboration. The authors developed a land-use planning model based on the ES concept that links thermodynamic concepts, mathematical equilibrium, metabolic theory and landscape metrics in a novel bio-energy framework, and discuss the potential of the tool to support impact assessment processes and landscape and urban planning at different spatial scales. 3. Conclusion Taken together, the contributions have shown that the process and results of ES indicator development for decision making is not only a scientific process. The way how ES indicators are framed and defined pre-determines what is being assessed and how this assessment can be used in policy, planning and decision making. It is unavoidable and even necessary to include the various value, knowledge and belief systems of decision makers or participants in decision processes. Consequently it is also important to reflect associated normative biases. In the special issue, best practice examples are introduced, particularly in the context of transdisciplinary approaches. Here attention is paid to representativeness of stakeholders, and hence legitimacy and continuous reflection throughout the process allows making biases transparent and addressable. Another challenge in this context is the balance between practicability and oversimplification, particularly with respect to data availability. Some authors and particularly decision makers argue to take into consideration indicators which can be fed by data that are collected already in existing monitoring approaches. Other authors argue that this often does not correspond to good scientific practice ignoring issues in indicator validity and the risk of focusing on those services which are perceived to be currently most important, visible and accessible. This situation is worsened, when indicators are supposed to inform decision making across various scales. An institutionalized, long-term, science-policy dialogue to arrive at a co-design of indicators to inform a multi-level ES governance approach could help here. Furthermore, social and environmental monitoring approaches, e.g. in the context of LongTerm-Social-Ecological-Research (LTSER) sites (Singh et al., 2010) need to be adapted towards delivering the requested indicator information. To account for the complexity of decision contexts one approach could be to bundle and aggregate indicator systems to social-ecological indices. Such indices could contribute also to services that are not visible and difficult to assess but are relevant for the well-being of future generations. An additional aspect is that such indices account better for aspect such as landscape composition and configuration, which are currently often ignored (Fürst et al., 2013).

Please cite this article in press as: Hauck, J., et al., Developing and applying ecosystem service indicators in decision-support at various scales. Ecol. Indicat. (2015), http://dx.doi.org/10.1016/j.ecolind.2015.09.037

G Model ECOIND-2652; No. of Pages 5

ARTICLE IN PRESS Special Issue Editorial / Ecological Indicators xxx (2015) xxx–xxx

4

Increasing ES literacy of decision makers is necessary and iterative, learning-by-doing approaches in transdisciplinary processes proved to be useful. In this process conceptual frameworks of ES and even the indicators themselves can be used as boundary objects to make explicit the implications of indicators. However, the ES concept by now became very complex with manifold ambiguities. While decision makers need to have a certain degree of ES literacy, good knowledge brokers on both the scientific but also at the policy side are needed to translate complexities and ambiguities into understandable and decidable questions.

References Albert, C., Bonn, A., Burkhard, B., Daube, S., Dietrich, K., Engels, B., Frommer, J., Götzl, M., Grêt-Regamey, A., Job-Hoben, B., Koellner, T., Marzelli, S., Moning, C., Müller, F., Rabe, S.-E., Ring, I., Schwaiger, E., Schweppe-Kraft, B., Wüstemann, H., 2015a. Towards a national set of ecosystem service indicators: in-sights from a biodiversity policy-driven approach in Germany. Ecol. Indicators. Albert, C., Galler, C., Hermes, J., Neuendorf, F., von Haaren, C., Lovett, A., 2015b. Applying ecosystem services indicators in landscape planning and management: the ES-in-Planning framework. Ecol. Indicators. Albert, C., Hauck, J., Buhr, N., von Haaren, C., 2014. What ecosystem services information do users want? Investigating interests and requirements among landscape and regional planners in Germany. Landsc. Ecol. Bagstad, K.J., Semmens, D., Waage, S., Winthrop, R., 2013. A comparative assessment of tools for ecosystem services quantification and valuation. Ecosyst. Serv. 5, 27–39. Bastian, O., Haase, D., Grunewald, K., 2012. Ecosystem properties, potentials and services – the EPPS conceptual framework and an urban application example. Ecol. Indicators 21, 7–16. Burkhard, B., Kroll, F., Nedkov, S., Muller, F., 2012. Mapping ecosystem service supply, demand and budgets. Ecol. Indicators 21, 17–29. Daily, G.C., Polasky, S., Goldstein, J., Kareiva, P.M., Mooney, H.A., Pejchar, L., Ricketts, T.H., Salzman, J., Shallenberger, R., 2009. Ecosystem services in decision-making: time to deliver. Front. Ecol. Environ. 7 (1), 21–28. Dick, J., Maes, J., Smith, R.I., Paracchini, M.L., Zulian, G., 2014. Cross-scale analysis of ecosystem services identified and assessed at local and European level. Ecol. Indicators 38, 20–30. Diehl, K.E., Burkhard, D., Jacob, K., 2015. Should the ecosystem services concept be used in European Commission impact assessment? Ecol. Indicators. ESID, 2012. Ecosystem Service Indicators Database. World Resources Institute, Washington, DC, http://www.esindicators.org/ (accessed 08/31/2014). Failing, L., Gregory, R., 2003. Ten common mistakes in designing biodiversity indicators for forest policy. J. Environ. Manag. 68, 121–132. Frank, S., Fürst, C., Japelj, A., Simonˇciˇc, P., Eyvindson, K., Kangas, A., Rasinmäki, J., Trubins, R., Eriksson, L.-O., Barkowski, R., 2015. RegioPower – an online platform for interactive tools for participatory land assessment and planning. GLP Newslett. 2015 (1). Fürst, C., Opdam, P., Inostroza, L., Luque, S., 2014. A balance score card tool for assessing how successful the ecosystem services concept is applied in participatory land use planning. Landsc. Ecol. 29 (8), 1435–1446. Fürst, C., Helming, K., Lorz, C., Müller, F., Verburg, P., 2013. Integrated land use and regional re-source management – a cross-disciplinary dialogue on future perspectives for a sustainable development of regional resources. J. Environ. Manag. 127, 1–5. Fürst, C., Pietzsch, K., Frank, S., Witt, A., Koschke, L., Makeschin, F., 2012. How to better consider sectoral planning information in regional planning - example afforestation and conversion. J. Environ. Plann. Manag. 2012, 1–29. Fürst, C., Volk, M., Makeschin, F., 2010. Squaring the circle – how to combine models, indicators, experts and end-users for integrated land use management support? Environ. Manag. 46 (6), 829–833. Haase, D., Larondelle, N., Andersson, E., Artmann, M., Gomez-baggethun, E., Hansen, R., Elmqvist, T., 2014. A quantitative review of urban ecosystem service assessments: concepts, models, and implementation. AMBIO: J. Hum. Environ. Haines-Young, R., Potschin, M., 2010. The links between biodiversity, ecosystem services and human well-being. In: Raffaelli, D., Frid, C. (Eds.), Ecosystem Ecology: A New Synthesis. BES Ecological Reviews Series, CUP, Cambridge, p. 172. Hauck, J., Schweppe-Kraft, B., Albert, C., Görg, C., Jax, K., Jensen, R., Fürst, C., Maes, J., Ring, I., Hönigová, I., Burkhard, B., Mehring, M., Tiefenbach, M., Grunewald, K., Schwarzer, M., Meurer, M., Sommerhäuser, M., Priess, J.A., Schmidt, J., GrêtRegamey, A., 2013. The promise of the ecosystem services concept for planning and decision-making. GAIA 22 (4), 232–236. Heink, U., Hauck, J., Jax, K., Sukopp, U., 2015. Requirements for the selection of ecosystem service indicators – the case of MAES indicators. Ecol. Indicators. Heink, U., Kowarik, I., 2010. What criteria should be used to select biodiversity indicators? Biodiv. Conserv. 19 (13), 3769–3797. Helfenstein, J., Kienast, F., 2014. Ecosystem service state and trends at the regional to national level: a rapid assessment. Ecol. Indicators 36, 11–18. Kandziora, M., Burkhard, B., Müller, F., 2013. Interactions of ecosystem properties, ecosystem integrity and ecosystem service indicators – a theoretical matrix exercise. Ecol. Indicators 28, 54–78.

Koschke, L., Fürst, C., Frank, S., Makeschin, F., 2012. A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision for planning support. Ecol. Indicators 21, 54–66. Lang, D., Wiek, A., Bergmann, M., Stauffacher, M., Martens, P., Moll, P., Swilling, M., Thomas, C.J., 2012. Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustain. Sci. 7 (Suppl. 1), 25–43. La Rosa, D., Spyra, M., Inostroz, L., 2015. Indicators of Cultural Ecosystem Services for urban planning: a review. Ecol. Indicators. Larigauderie, A., Mooney, A.H., 2010. The Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services: moving a step closer to an IPCC-like mechanism for biodiversity. Curr. Opin. Environ. Sustain. 2 (1–2), 9–14. Lyytimäki, J., Sipilä, M., 2009. Hopping on one leg – the challenge of ecosystem disservices for urban green management. Urban For. Urban Green. 8, 309–315. Maes, J., Teller, A., Erhard, M., Murphy, P., Paracchini, M.L., et al., 2014. Mapping and Assessment of Ecosystems and their Services. Indicators for Ecosystem Assessments under Action 5 of the EU Biodiversity Strategy to 2020. 2nd Report. European Union, Brussels, 80 p. Mononen, L., Auvinen, A.-P., Ahokumpu, A.-L., Rönkä, M., Aarras, N., Tolvanen, H., Kamppinen, M., Viirret, E., Kumpula, T., Vihervaara, P., 2015. National ecosystem service indicators: measures of social–ecological sustainability. Ecol. Indicators. Norgrove, L., Hauser, S., 2015. Biophysical criteria used by farmers for fallow selection in West and Central Africa. Ecol. Indicators. Norton, L.R., Greene, S., Scholefield, P., Dunbar, M.J., 2015. The importance of scale in the development of ecosystem service indicators. Ecol. Indicators. Opdam, P., Albert, C., Fürst, C., Gret-Regamey, A., Kleemann, J., Parker, D.C., la Rosa, D., Schmidt, K., Villamor, G.B., Walz, A., 2015. Ecosystem services for connecting actors – lessons from a symposium. CASES – Change Adapt. Soc.-Ecol. Syst. 2, 1–7. Pelorosso, R., Gobattoni, F., Geri, F., Monaco, R., Leone, A., 2015. Evaluation of Ecosystem Services related to Bio-Energy Landscape Connectivity (BELC) for land use decision making across different planning scales. Ecol. Indicators. Potschin, M.B., Haines-Young, R.H., 2011. Ecosystem services: exploring a geographical perspective. Prog. Phys. Geogr. 35, 575–594. Priess, J., Elger, R., Hauck, J., 2014. The ESM-App – a new smartphone application to map ecosystem services. In: Ames, D.P., Quinn, N.W.T., Rizzoli, A.E. (Eds.), Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs). June 15–19, San Diego, CA, USA. International Environmental Modelling and Software Society (iEMSs), Manno, pp. 1–7. Ruckelshaus, M., McKenzie, E., Tallis, H., Guerry, A., Daily, G., Kareiva, P., Polasky, S., Ricketts, T., Bhagabati, N., Wood, S.A., Bernhardt, J., 2013. Notes from the field: lessons learned from using ecosystem service approaches to inform real-world decisions. Ecol. Econ. Saarela, S.-R., Rinne, J., 2015. Knowledge brokering and boundary work for ecosystem service indicators: an urban case study in Finland. Ecol. Indicators. Schröter, M., van der Zanden, E.H., van Oudenhoven, A.P.E., Remme, R.P., SernaChavez, H.M., de Groot, R.S., Opdam, P., 2014. Ecosystem services as a contested concept: a synthesis of critique and counter-arguments. Conserv. Lett., 1–23. Singh, S.J., Haberl, H., Gaube, V., Grünbühel, C.M., Lisivieveci, P., Lutz, J., Matthews, R., Mirtl, M., Vadineanu, A., Wildenberg, M., 2010. Conceptualising long-term socio-ecological research (LTSER): integrating the social dimension. Long-term Ecol. Res., 377–398. Spyra, M., 2014. Ecosystem services and border regions. Case study from Czech–Polish Borderland. J. Land Use Mobil. Environ. 4 (3) http://www.tema. unina.it/index.php/tema/article/view/2543/2567. Stauffacher, M., Krütli, P., Flüeler, T., Scholz, R.W., 2012. Learning from the transdisciplinary case study approach: a functional-dynamic approach to collaboration among diverse actors in applied energy settings. In: Sprenger, D., Flüeler, T., Goldblatt, D.L., Minsch, J. (Eds.), Tackling Long-Term Global Energy Problems, Environment & Policy, vol. 52. Springer, pp. 227–245, http://dx.doi.org/10.1007/ 978-94-007-2333-7 11. Tratalos, J.A., Haines-Young, R., Potschin, M., Fish, R., Church, A., 2015. Cultural ecosystem services in the UK: lessons on designing indicators to inform management and policy. Ecol. Indicators. Turkelboom, F., Raquez, P., Dufrene, M., Raes, L., Simoens, I., Jacobs, S., Stevens, M., Vreese, R., De Panis, J., Hermy, M., Thoonen, M., Liekens, I., Fontaine, C., Dendoncker, N., Biest, K., van der Casaer, J., Heyrman, H., Meiresonne, L., Keune, H., Dufrène, M., Van der Biest, K., 2013. CICES going local: ecosystem services classification adapted for a highly populated country. In: Jacobs, S., Dendoncker, N., Keune, H. (Eds.), Ecosystem Services: Global Issues, Local Practices. Elsevier, New York, pp. 223–247. Turnhout, E., Hisschemöller, M., Eijsackers, H., 2007. Ecological indicators: between the two fires of science and policy. Ecol. Indicators 7 (2), 215–228. Turnhout, E., Stuiver, M., Klostermann, J., Harms, B., Leeuwis, C., 2013. New roles of science in society: different repertoires of knowledge brokering. Sci. Public Policy 40, 354–365. UK NEA – The UK National Ecosystem Assessment, 2011. The UK National Ecosystem Assessment: Technical Report. UNEP WCMC, Cambridge. van Oudenhoven, A.P.E., Petz, K., Alkemade, R., Hein, L., de Groot, R.S., 2012. Framework for systematic indicator selection to assess effects of land management on ecosystem services. Ecol. Indicators 21, 110–122. Van Reeth, W., 2013. Ecosystem service basics: ecosystem service indicators: are we measuring what we want to manage? In: Jacobs, S., Dendoncker, N., Keune, H. (Eds.), Ecosystem Services: Global Issues, Local Practices. Elsevier, pp. 41–58.

Please cite this article in press as: Hauck, J., et al., Developing and applying ecosystem service indicators in decision-support at various scales. Ecol. Indicat. (2015), http://dx.doi.org/10.1016/j.ecolind.2015.09.037

G Model ECOIND-2652; No. of Pages 5

ARTICLE IN PRESS Special Issue Editorial / Ecological Indicators xxx (2015) xxx–xxx

Wissen Hayek, U., Teich, M., Klein, T.M., Grêt-Regamey, A., 2015. Bringing ecosystem services indicators into spatial planning practice: lessons from collaborative development of a web-based visualization platform. Ecol. Indicators.

Guest Editor Jennifer Hauck a,b,∗ a Helmholtz Centre for Environmental Research – UFZ, Department of Ecosystem Services, Permoserstrasse 15, Leipzig 04318, Germany b German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig D-04103, Germany Guest Editor Christian Albert a,b a Helmholtz Centre for Environmental Research – UFZ, Department Environmental Politics, Permoserstrasse 15, Leipzig 04318, Germany b Leibniz Universität Hannover, Institute of Environmental Planning, Herrenhäuser Strasse 2, Hannover 30419, Germany Guest Editor Christine Fürst Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Germany

5

Guest Editor Davide Geneletti University of Trento, Department of Civil, Environmental and Mechanical Engineering, via Mesiano, 77, Trento 38123, Italy Guest Editor Daniele La Rosa University of Catania, Department Civil Engineering and Architecture, Via S. Sofia 64, Catania 95123, Italy Guest Editor Carsten Lorz Hochschule Weihenstephan-Triesdorf, University of Applied Sciences, Woods and Forestry, Hans-Carl-von-Carlowitz-Platz 3, Freising 85354, Germany Guest Editor Marcin Spyra Opole University of Technology, Department of Civil Engineering and Architecture, Katowicka 48, Opole 45-061, Poland ∗ Corresponding

author at: Helmholtz-Centre for Environmental Research – UFZ, Germany. Tel.: +49 341 9733199. E-mail address: [email protected] (J. Hauck)

Please cite this article in press as: Hauck, J., et al., Developing and applying ecosystem service indicators in decision-support at various scales. Ecol. Indicat. (2015), http://dx.doi.org/10.1016/j.ecolind.2015.09.037 View publication stats